ENGINEERING COMMITTEE
Data Standards Subcommittee
SCTE 24-21 2012
BV16 Speech Codec Specification for
Voice over IP Applications in Cable Telephony
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i
Contents
1
INTRODUCTION ...................................................................................................... 1
2
OVERVIEW OF THE BV16 SPEECH CODEC .......................................................... 1
2.1 Brief Introduction of Two-Stage Noise Feedback Coding (TSNFC)....................................... 1
2.2 Overview of the BV16 Codec ................................................................................................ 3
3
DETAILED DESCRIPTION OF THE BV16 ENCODER ............................................ 7
3.1 High-Pass Pre-Filtering ........................................................................................................ 7
3.2 Short-Term Linear Predictive Analysis................................................................................. 7
3.3 Conversion to LSP .............................................................................................................. 10
3.4 LSP Quantization ............................................................................................................... 12
3.5 Conversion to Short-Term Predictor Coefficients ............................................................... 17
3.6 Long-Term Linear Predictive Analysis (Pitch Extraction) .................................................. 18
3.7 Long-Term Predictor Parameter Quantization ................................................................... 26
3.8 Excitation Gain Quantization ............................................................................................. 27
3.9 Excitation Vector Quantization .......................................................................................... 31
3.10 Bit Multiplexing ................................................................................................................. 35
4
DETAILED DESCRIPTION OF THE BV16 DECODER .......................................... 36
4.1 Bit De-multiplexing............................................................................................................. 36
4.2 Long-Term Predictor Parameter Decoding ......................................................................... 36
4.3 Short-Term Predictor Parameter Decoding ........................................................................ 36
4.4 Excitation Gain Decoding ................................................................................................... 39
4.5 Excitation VQ Decoding and Scaling .................................................................................. 42
4.6 Long-Term Synthesis Filtering ........................................................................................... 42
4.7 Short-Term Synthesis Filtering ........................................................................................... 42
4.8 Example Postfilter .............................................................................................................. 43
4.9 Example Packet Loss Concealment ..................................................................................... 45
APPENDIX 1: GRID FOR LPC TO LSP CONVERSION ............................................... 48
APPENDIX 2: FIRST-STAGE LSP CODEBOOK .......................................................... 49
APPENDIX 3: SECOND-STAGE LSP SHAPE CODEBOOK ......................................... 52
APPENDIX 4: PITCH PREDICTOR TAB CODEBOOK ............................................... 54
APPENDIX 5: GAIN CODEBOOK ................................................................................ 55
APPENDIX 6: GAIN CHANGE THRESHOLD MATRIX .............................................. 56
APPENDIX 7: EXCITATION VQ SHAPE CODEBOOK ............................................... 57
ii
Figures
Figure 1 Basic codec structure of Two-Stage Noise Feedback Coding (TSNFC) ...................... 2
Figure 2 An alternative codec structure of Two-Stage Noise Feedback Coding (TSNFC) .......... 3
Figure 3 Block diagram of the BV16 encoder ....................................................................... 4
Figure 4 Block diagram of the BV16 decoder ....................................................................... 6
Figure 5 Short-term linear predictive analysis and quantization (block 210) ............................. 8
Figure 6 LSP quantizer (block 216)................................................................................... 13
Figure 7 Long-term predictive analysis and quantization (block 220) .................................... 19
Figure 8 Prediction residual quantizer (block 230) .............................................................. 28
Figure 9 Filter structure used in excitation VQ codebook search .......................................... 32
Figure 10 Bit stream format ............................................................................................. 35
Figure 11 Short-term predictor parameter decoder (block 420) ............................................ 37
Figure 12 Excitation gain decoder..................................................................................... 40
Tables
Table 1 Bit allocation of the BV16 codec ............................................................................ 6
iii
1
INTRODUCTION
This document contains the description of the BV16 speech codec 1. BV16 compresses 8 kHz
sampled narrowband speech to a bit rate of 16 kb/s by employing a speech coding algorithm called
Two-Stage Noise Feedback Coding (TSNFC), developed by Broadcom.
The rest of this document is organized as follows. Section 2 gives a high-level overview of the
TSNFC algorithm. Sections 3 and 4 give detailed description of the BV16 encoder and decoder,
respectively. The BV16 codec specification given in Sections 3 and 4 contain sufficient details to
allow those skilled in the art to implement bit-stream compatible and functionally equivalent
BV16 encoders and decoders.
2
OVERVIEW OF THE BV16 SPEECH CODEC
In this section, the general principles of Two-Stage Noise Feedback Coding (TSNFC) are first
introduced. Next, an overview of the BV16 algorithm is given.
2.1
Brief Introduction of Two-Stage Noise Feedback Coding (TSNFC)
In conventional Noise Feedback Coding (NFC), the encoder modifies a prediction residual signal
by adding a noise feedback signal to it. A scalar quantizer quantizes this modified prediction
residual signal. The difference between the quantizer input and output, or the quantization error
signal, is passed through a noise feedback filter. The output signal of this filter is the noise
feedback signal added to the prediction residual. The noise feedback filter is used to control the
spectrum of the coding noise in order to minimize the perceived coding noise. This is achieved by
exploiting the masking properties of the human auditory system.
Conventional NFC codecs typically use only a short-term noise feedback filter to shape the
spectral envelope of the coding noise, and a scalar quantizer is used universally. In contrast,
Broadcom’s Two-Stage Noise Feedback Coding (TSNFC) system uses a codec structure
employing two stages of noise feedback coding in a nested loop: the first NFC stage performs
short-term prediction and short-term noise spectral shaping (spectral envelope shaping), and the
second nested NFC stage performs long-term prediction and long-term noise spectral shaping
(harmonic shaping). Such a nested two-stage NFC structure is shown in Figure 1 below.
1
The “BV16 speech codec” specification is based on Broadcom Corporation’s BroadVoice®16 speech codec. The BroadVoice®
open source software is provided under the GNU Lesser General Public License (“LGPL”), version 2.1, as published by the Free
Software Foundation.
-1-
Input
signal s(n ) d (n )
+
+
+
-
Quantizer
+
+
-
+
Ps (z )
short-term
predictor
uq(n )
u(n )
v(n )
+
+
Output
signal
-
Ps (z )
q(n )
short-term
predictor
+
N l ( z) − 1
+
sq(n )
vq(n )
long-term
noise feedback
filter
Pl (z )
+
Fs (z )
-
+
long-term
predictor
qs(n )
short-term
noise feedback
filter
Figure 1 Basic codec structure of Two-Stage Noise Feedback Coding (TSNFC)
In Figure 1 above, the outer layer (including the two short-term predictors and the short-term noise
feedback filter) follows the structure of the conventional NFC codec. The TSNFC structure in
Figure 1 is obtained by replacing the simple scalar quantizer in the conventional (single-stage)
NFC structure by a “predictive quantizer” that employs long-term prediction and long-term noise
spectral shaping. This “predictive quantizer” is represented by the inner feedback loop in Figure 1,
including the long-term predictor and long-term noise feedback filter. This inner feedback loop
uses an alternative but equivalent conventional NFC structure, where N l (z ) represents the filter
whose frequency response is the desired noise shape for long-term noise spectral shaping. In the
outer layer, the short-term noise feedback filter Fs (z ) is usually chosen as a bandwidth-expanded
version of the short-term predictor Ps (z ) . The choice of different NFC structures in the outer and
inner layers is based on complexity consideration. By combining two stages of NFC in a nested
loop, the TSNFC in Figure 1 can reap the benefits of both short-term and long-term prediction and
also achieve short-term and long-term noise spectral shaping at the same time.
It is natural and straightforward to use a scalar quantizer in Figure 1. However, to achieve better
coding efficiency, a vector quantizer is used in BV16. In the Vector Quantization (VQ) codebook
search, the u(n ) vector cannot be generated before the VQ codebook search starts. Due to the
feedback structure in Figure 1, the elements of u(n ) from the second element on will depend on
the vector-quantized version of earlier elements. Therefore, the VQ codebook search is performed
by trying out each of the candidate codevectors in the VQ codebook (i.e. fixing a candidate uq(n )
vector first), calculating the corresponding u(n ) vector and the corresponding VQ error
q( n ) = u( n ) − uq( n ) . The VQ codevector that minimizes the energy of q(n ) within the current
vector time span is chosen as the winning codevector, and the corresponding codebook index
becomes part of the encoder output bit stream for the current speech frame.
-2-
The TSNFC decoder structure is simply a quantizer decoder followed by the two feedback filter
structures involving the long-term predictor and the short-term predictor, respectively, shown on
the right half of Figure 1. Thus, the TSNFC decoder is similar to the decoders of other predictive
coding techniques such as Adaptive Predictive Coding (APC), Multi-Pulse Linear Predictive
Coding (MPLPC), and Code-Excited Linear Prediction (CELP).
If the alternative NFC structure in the inner feedback loop of Figure 1 is also used in the outer
feedback loop, an alternative TSNFC codec structure is obtained, as shown in Figure 2 below.
Here N s (z ) represents a short-term filter whose frequency response is the desired noise shape for
short-term noise spectral shaping. The codec structure in Figure 2 is mathematically equivalent to
the structure in Figure 1, but it allows direct specification of the short-term noise spectral shape as
defined by N s (z ) . This can be an advantage in some applications.
Input
signal s(n )
+
+
-
uq(n )
u(n )
v(n )
+
+
-
+
+
N l ( z) − 1
Output
signal
-
Ps (z )
q(n )
short-term
predictor
+
long-term
noise feedback
filter
Pl (z )
+
+
+
+
Quantizer
sq(n )
vq(n )
N s ( z) − 1
-
+
long-term
predictor
qs(n )
short-term
noise feedback filter
Figure 2 An alternative codec structure of Two-Stage Noise Feedback Coding (TSNFC)
2.2
Overview of the BV16 Codec
The BV16 codec is a purely forward-adaptive TSNFC codec. It operates at an input sampling rate
of 8 kHz and an encoding bit rate of 16 kb/s, or 2 bits per sample. BV16 uses a frame size of 5
ms, or 40 samples. There is no look ahead. Therefore, the total algorithmic buffering delay is just
the frame size itself, or 5 ms. The main design goal of BV16 is to make the coding delay and the
codec complexity as low as possible, while providing toll speech quality exceeding or equivalent
to that of G.728 and G.729E.
-3-
The block diagram of the BV16 encoder is shown in Figure 3. It is based on the alternative
TSNFC codec structure shown in Figure 2. The BV16 decoder block diagram is shown in Figure
4.
LSPI
210
220
295
Short-term
predictive
analysis &
quantization
Long-term
predictive
analysis &
quantization
PPI
PPTI
GI
s(n)
Output bit stream
Bit
multiplexer
CI
pp, ppt
203
Input
signal High-pass
pre-filter
+
+
-
230
255
v(n)
+
+
275
u(n)
245
285
Prediction
residual
quantizer
uq(n)
+
dq(n)
280
-
Shortterm
predictor
+
265
270
+
ltnf(n)
sq(n)
240
+
+
q(n)
Long-term
noise
feedback
filter
260
ppv(n)
+
+
Longterm
predictor
290
-
250
+
253
stnf(n)
qs(n)
Short-term
noise feedback
filter
Figure 3 Block diagram of the BV16 encoder
Due to the small frame size, the parameters of the short-term predictor (also called the “LPC
predictor”) and the long-term predictor (also called the “pitch predictor”) are both transmitted and
updated once a frame. The gain of the excitation signal is transmitted once every frame. The
excitation VQ uses a vector dimension of 4 samples. Hence, there are 10 excitation vectors in a
frame.
The BV16 encoder first passes the input signal through a fixed pole-zero high-pass pre-filter to
remove possible DC bias or low frequency rumble. The resulting signal is then used to derive the
LPC predictor coefficients.
To keep the complexity low, BV16 uses a relatively low LPC predictor order of 8, and the LPC
analysis window is 20 ms (160 samples) long. The LPC analysis window is asymmetric, with the
peak of the window located at the center of the current frame, and the end of the window
coinciding with the last sample of the current frame. Autocorrelation LPC analysis based on
-4-
Levinson-Durbin recursion is used to derive the coefficients of the 8th-order LPC predictor. The
derived LPC predictor coefficients are converted to Line-Spectrum Pair (LSP) parameters, which
are then quantized by an inter-frame predictive coding scheme.
The inter-frame prediction of LSP parameters uses an 8th-order moving-average (MA) predictor.
The MA predictor coefficients are fixed. The time span that this MA predictor covers is 8 × 5 ms
= 40 ms. The inter-frame LSP prediction residual is quantized by a two-stage vector quantizer.
The first stage employs an 8-dimensional vector quantizer with a 7-bit codebook. The second stage
uses an 8-dimensional sign-shape VQ with 1 bit for sign and 6 bits for shape.
For long-term prediction, a three-tap pitch predictor with an integer pitch period is used. To keep
the complexity low, the pitch period and the pitch taps are both determined in an open-loop
fashion.
The three pitch predictor taps are jointly quantized using a 5-bit vector quantizer. The distortion
measure used in the codebook search is the energy of the open-loop pitch prediction residual. The
32 codevectors in the pitch tap codebook have been “stabilized” to make sure that they will not
give rise to an unstable pitch synthesis filter.
The excitation gain is also determined in an open-loop fashion to keep the complexity low. The
average power of the open-loop pitch prediction residual within the current frame is calculated and
converted to the logarithmic domain. The resulting log-gain is then quantized using intersubframe MA predictive coding. The MA predictor order for the log-gain is 8, corresponding to a
time span of 8 × 5 = 40 ms. Again, the log-gain MA predictor coefficients are fixed. The log-gain
prediction residual is quantized by a 4-bit scalar quantizer.
The 4-dimensional excitation VQ codebook has a simple sign-shape structure, with 1 bit for sign,
and 4 bits for shape. In other words, only 16 four-dimensional codevectors are stored, but the
mirror image of each codevector with respect to the origin is also a codevector.
In the BV16 decoder, the decoded excitation vectors are scaled by the excitation gain. The scaled
excitation signal passes through a long-term synthesis filter and a short-term synthesis filter.
Figure 4 shows the block diagram of the BV16 decoder.
-5-
Long-term
synthesis filter
400
Input
bit stream
430
GI
Bit
de-multiplexer
CI
Prediction
residual uq(n)
quantizer
decoder
450
PPTI
475
Output
signal
470
sq(n)
dq(n)
+
+
440
Longterm
predictor
410
PPI
455
Short-term
synthesis filter
460
Shortterm
predictor
Long-term
predictive
parameter
decoder
420
Short-term
predictive
parameter
decoder
LSPI
Figure 4 Block diagram of the BV16 decoder
Table 1 shows the bit allocation of BV16 in each 5 ms frame. The LSP parameters are encoded
into 14 bits per frame, including 7 bits for the first-stage VQ, and 1 + 6 = 7 bits for the secondstage sign-shape VQ. The pitch period and pitch predictor taps are encoded into 7 and 5 bits,
respectively. The excitation gain in each frame is encoded into 4 bits. The 10 excitation vectors
are each encoded with 1 bit for sign and 4 bits for shape, resulting in 50 bits per frame for
excitation VQ. Including the other 30 bits of side information, the grand total is 80 bits per 40sample frame, which is 2 bits/sample, or 16 kb/s.
Table 1 Bit allocation of the BV16 codec
Parameter
Bits per frame (40 samples)
LSP
7 + 7 = 14
Pitch Period
7
3 Pitch Predictor Taps
5
Excitation Gain
4
10 Excitation Vectors
(1 + 4) × 10 = 50
Total
80
-6-
3
DETAILED DESCRIPTION OF THE BV16 ENCODER
In this section, detailed description of each functional block of the BV16 encoder in Figure 3 is
given. When necessary, certain functional blocks will be expanded into more detailed block
diagrams. The description given in this section will be in sufficient detail to allow those skilled in
the art to implement a mathematically equivalent BV16 encoder.
3.1
High-Pass Pre-Filtering
Refer to Figure 3. The input signal is assumed to be represented by 16-bit linear PCM. Block 203
is a high-pass pre-filter with fixed coefficients. It is a second-order pole-zero filter with the
following transfer function.
H hpf ( z ) =
0.924133 − 1.848267 z −1 + 0.924133 z −2
1 − 1.899109 z −1 + 0.905396 z − 2
This high-pass pre-filter removes undesirable low-frequency components from the input signal.
3.2
Short-Term Linear Predictive Analysis
The high-pass filtered signal s(n ) is buffered at block 210, which performs short-term linear predictive
analysis and quantization to obtain the coefficients for the short-term predictor 240 and the short-term
noise feedback filter 250. This block 210 is further expanded in Figure 5.
Refer to Figure 5. The input signal s(n ) is buffered in block 211, where a 20 ms asymmetric
analysis window is applied to the buffered s(n ) signal array. The “left window” is 17.5 ms long,
and the “right window” is 2.5 ms long. Let LWINSZ be the number of samples in the left window
(LWINSZ = 140 for 8 kHz sampling), then the left window is given by
wl ( n ) =
1
nπ
1 − cos
, n = 1, 2, …, LWINSZ.
2
LWINSZ + 1
Let RWINSZ be the number of samples in the right window. Then, RWINSZ = 20 for 8 kHz
sampling. The right window is given by
( n − 1)π
wr ( n ) = cos
, n = 1, 2, …, RWINSZ .
2 RWINSZ
The concatenation of wl(n) and wr(n) gives the 20 ms asymmetrical analysis window, with the
peak of the window located at the center of the current frame. When applying this analysis
window, the last sample of the window is lined up with the last sample of the current frame.
Therefore, the codec does not use any look ahead.
-7-
To block 221 To block 240
LSPI
210
a′i
217
218
a~i
Bandwidth
expansion
Convert to
predictor
coefficients
Apply window
& calculate
autocorrelation
LSP
quantizer
r (i )
White noise
correction
& spectral
smoothing
li
Convert
to LSP
ai
212
211
215
216
~
li
213
rˆ(i )
LevinsonDurbin
recursion
214
âi
Bandwidth
expansion
α̂ i
To block 250
βˆ
i
s(n )
Figure 5 Short-term linear predictive analysis and quantization (block 210)
More specifically, without loss of generality, let the sampling time index range of n = 1, 2, …,
FRSZ corresponds to the current frame, where the frame size FRSZ = 40. Then, the s(n) signal
buffer stored in block 211 is for n = -119, -118, …, -1, 0, 1, 2, …, 40. The asymmetrical LPC
analysis window function can be expressed as
wl ( n + 120),
w( n ) =
wr ( n − 20),
n = −119, − 118, ..., 20
.
n = 21, 22, ..., 40
The windowing operation is performed as follows.
sw ( n ) = s( n )w( n ), n = -119, -118, …, -1, 0, 1, 2, …, 40.
Next, block 211 calculates the autocorrelation coefficients as follows.
r (i ) =
40
∑s
w
n = −119 + i
( n ) sw ( n − i ) , i = 0, 1, 2, …, 8.
The calculated autocorrelation coefficients are passed to block 212, which applies a Gaussian
window to the autocorrelation coefficients to perform spectral smoothing. The Gaussian window
function is given by
-8-
(
− 2 π iσ / f s
gw(i ) = e
)2
2
, i = 1, 2, …, 8,
where f s is the sampling rate of the input signal, expressed in Hz, and σ is 40 Hz.
After multiplying the r(i) array by such a Gaussian window, block 212 then multiplies r(0) by a
white noise correction factor of WNCF = 1 + ε , where ε = 0.0001. In summary, the output of
block 212 is given by
i=0
1.0001 × r(0),
rˆ(i ) =
i = 1,2,...,8
gw(i )r (i ),
Block 213 performs the Levinson-Durbin recursion to convert the autocorrelation coefficients rˆ(i )
to the short-term predictor coefficients âi , i = 0, 1, …, 8. If the Levinson-Durbin recursion exits
pre-maturely before the recursion is completed (for example, because the prediction residual
energy E(i) is less than zero), then the short-term predictor coefficients of the last frame is also
used in the current frame. To do the exception handling this way, there needs to be an initial value
of the âi array. The initial value of the âi array is set to aˆ0 = 1 and âi = 0 for i = 1, 2, …, 8. The
Levinson-Durbin recursion is performed in the following algorithm.
1. If rˆ(0) ≤ 0 , use the âi array of the last frame, and exit the Levinson-Durbin recursion.
2. E (0) = rˆ(0)
3. k1 = − rˆ(1) / rˆ(0)
(1)
4. aˆ1 = k1
5. E (1) = (1 − k1 ) E (0)
6. If E (1) ≤ 0 , use the âi array of the last frame, and exit the Levinson-Durbin recursion.
7. For i = 2, 3, 4, …, 8, do the following
2
i −1
ki =
aˆi
− rˆ(i ) − ∑ aˆ j
rˆ(i − j )
j =1
E (i − 1)
(i )
= ki
(i )
= aˆ j
aˆ j
( i −1)
( i −1)
( i −1)
+ ki aˆi − j
, for j = 1, 2, ..., i − 1
E (i ) = (1 − ki ) E (i − 1)
If E (i ) ≤ 0, use the aˆi array of the last frame, and exit the Levinson - Durbin recursion.
2
If the recursion is exited pre-maturely, the âi array of the last frame is used as the output of block
213. If the recursion is completed successfully (which is normally the case), then the final output
of block 213 is taken as
-9-
aˆ0 = 1
(8)
aˆi = aˆi , for i = 1, 2, …, 8
Block 214 performs bandwidth expansion as follows
ai = (0.96852)i aˆi , for i = 0, 1, …, 8.
In addition, it also performs bandwidth expansion operations to derive the coefficients of the shortterm noise feedback filter (block 250). Block 250 in Figure 3 has a transfer function of
8
Fs ( z ) = N s ( z ) − 1 =
∑ βˆ
z −i
∑αˆ
−i
i
i =1
8
.
i
z
i =0
Block 214 calculates the coefficients of Fs (z ) as
αˆi = (0.85)i âi
, for i = 0, 1, …, 8,
βˆi = [(0.5)i − (0.85)i ] âi
3.3
, for i = 1, 2, …, 8.
Conversion to LSP
In Figure 5, block 215 converts the LPC coefficients ai , i = 1, 2,, 8 of the prediction error filter
given by
8
A( z ) = 1 + ∑ ai z − i
i =1
to a set of 8 Line-Spectrum Pair (LSP) coefficients li , i = 1, 2,, 8 . The LSP coefficients, also
known as the Line Spectrum Frequencies (LSF), are the angular positions normalized to 1, i.e. 1.0
corresponds to the Nyquist frequency, of the roots of
Ap ( z ) = A( z ) + z −9 A( z −1 )
and
Am ( z ) = A( z ) − z −9 A( z −1 )
- 10 -
on the upper half of the unit circle, z = e jω , 0 ≤ ω ≤ π , less the trivial roots in z = −1 and z = 1 of
Ap (z ) and Am (z ) , respectively. Due to the symmetry and anti-symmetric of Ap (z ) and Am (z ) ,
respectively, the roots of interest can be determined as the roots of
4
G p (ω ) = ∑ g p ,i cos(iω )
i =0
and
4
Gm (ω ) = ∑ g m,i cos(iω )
i =0
where
f p |m, 4
g p |m,i =
2 f p |m, 4 − i
i=0
i = 1,,4
in which
i=0
1.0
f p ,i =
ai + a9 − i − f p ,i −1 i = 1,,4
and
i=0
1.0
.
f m,i =
ai − a9−i + f p ,i −1 i = 1, ,4
The subscript "p|m" means dual versions of the equation exist, with either subscript "p" or
subscript "m". The roots of Ap (z ) and Am (z ) , and therefore the roots of G p (ω ) and Gm (ω ) , are
interlaced, with the first root belonging to G p (ω ) . The evaluation of the functions G p (ω ) and
Gm (ω )
are carried out efficiently using Chebyshev polynomial series.
x = cos(ω ) ,
With the mapping
cos( mω ) = Tm (x )
where Tm ( x ) is the mth-order Chebyshev polynomial, the two functions G p (ω ) and Gm (ω ) can be
expressed as
4
G p |m ( x ) = ∑ g p |m,i Ti ( x ) .
i =0
- 11 -
Due to the recursive nature of Chebyshev polynomials the functions can be evaluated as
G p|m ( x ) =
b p|m,0 ( x ) − b p|m, 2 ( x ) + g p|m,0
2
where bp|m,0 ( x ) and bp|m, 2 ( x ) are calculated using the following recurrence
b p | m , i ( x ) = 2 x b p | m , i +1 ( x ) − b p | m , i + 2 ( x ) + g p | m , i
with initial conditions b p|m,5 ( x ) = b p|m,6 ( x ) = 0 .
The roots of G p (x ) and Gm (x ) are determined in an alternating fashion starting with a root in
G p (x ) . Each root of G p (x ) and Gm (x ) is located by identifying a sign change of the relevant
function along a grid of 60 points, given in Appendix 1. The estimation of the root is then refined
using 4 bisections followed by a final linear interpolation between the two points surrounding the
root. It should be noted that the roots and grid points are in the cosine domain. Once the 8 roots
xi = cos(ω i ),
i = 1, 2,, 8
are determined in the cosine domain, they are converted to the normalized frequency domain
according to
li = cos −1 ( xi ) π ,
i = 1, 2,, 8
in order to obtain the LSP coefficients. In the rare event that less than 8 roots are found, block 215
returns the LSP coefficients of the previous frame, li ( k − 1), i = 1, 2,, 8 , where the additional
parameter k represents the frame index of the current frame. The LSP coefficients of the previous
frame at the very first frame are initialized to
li (0) = i / 9,
3.4
i = 1, 2,, 8 .
LSP Quantization
Block 216 of Figure 5 vector-quantizes and encodes the LSP coefficient vector,
The output LSP quantizer index array,
l = [l1 l2 l8 ] T , to a total of 14 bits.
LSPI = {LSPI 1 , LSPI 2 }, is passed to the bit multiplexer (block 295), while the quantized LSP
~
~ ~ ~
coefficient vector, l = [ l1 l2 l8 ] T , is passed to block 217.
- 12 -
The LSP quantizer is based on mean-removed inter-frame moving-average (MA) prediction with
two-stage vector quantization (VQ) of the prediction error. The quantizer enables bit-error
detection at the decoder by constraining the codevector selection at the encoder. It should be noted
that the encoder must perform the specified constrained VQ in order to maintain interoperability
properly. The first-stage VQ is searched using the simple mean-squared error (MSE) distortion
criterion, while the second-stage sign-shape VQ is searched using the weighted mean-square error
(WMSE) distortion criterion.
21614
LSP spacing
l
2163
l̂
order MA
prediction
l
Input LSP vector
+
~e
22
+
21611
Second stage VQ
8th
21612
e1 +
+
-
Quantized LSP vector
~e
2
21613
+
Mean LSP vector
~
l
+
l
2162
21615
2165
ê1
+
w
First stage VQ
2166
e2
Regular 8
dimensional
MSE VQ
~e
21
-
2164
+
e 22
+
8 dimensional
constrained
WMSE VQ with
signed codebook
2161
Calculate
LSP weights
w
LSPI1
Index sub-quantizer 1
LSPI2
Index sub-quantizer 2
Figure 6 LSP quantizer (block 216)
Block 216 is further expanded in Figure 6. The first-stage VQ takes place in block 2165, and the
second-stage constrained sign-shape VQ takes place in block 21615. Except for the LSP quantizer
indices LSPI 1 , LSPI 2 all signal paths in Figure 6 are for vectors of dimension 8. Block 2161 uses
the unquantized LSP coefficient vector to calculate the weights to be used later in the second-stage
WMSE VQ. The weights are determined as
i =1
1 /( l2 − l1 ),
wi = 1 / min(li − li −1 , li +1 − li ), 1 < i < 8 .
1 /(l − l ),
i =8
M
M −1
Basically, the i-th weight is the inverse of the distance between the i-th LSP coefficient and its
nearest neighbor LSP coefficient.
- 13 -
Adder 2162 subtracts the constant LSP mean vector,
l = [0.0950317 0.1489563 0.2513123 0.3629456 0.4780884 0.5877075 0.7058105 0.8007202]
T
,
from the unquantized LSP coefficient vector to get the mean-removed LSP vector,
e1 = l − l .
In Figure 6, block 2163 performs 8th order inter-frame MA prediction of the mean-removed LSP
vector e1 based on the ~e2 vectors in the previous 8 frames, where ~e2 is the quantized version of
the inter-frame LSP prediction error vector 2. Let ~e2,i ( k ) denote the i-th element of the vector ~e2 in
the frame that is k frames before the current frame. Let eˆ1,i be the i-th element of the inter-framepredicted mean-removed LSP vector ê1 . Then, block 2163 calculates the predicted mean-removed
LSP vector according to
T
eˆ1,i = p LSP ,i ⋅ [~
e2,i (1)
T
~
e2,i ( 2) ~
e2,i (3) ~
e2,i ( 4) ~
e2,i (5) ~
e2,i (6) ~
e2,i (7) ~
e2,i (8) ] , i = 1, 2, , 8 ,
where p LSP,i holds the 8 prediction coefficients for the i-th LSP coefficient and is given by
p LSP ,1T = [ 1.040710 0.844971 0.682922 0.575989
p LSP ,2T = [ 1.034851 0.884094 0.723816 0.609863
p LSP ,3T = [ 1.055237 0.922180 0.762695 0.644531
p LSP ,4T = [ 1.076843 0.935608 0.790771 0.673523
p LSP ,5T = [ 1.065552 0.901978 0.746155 0.636047
0.464600 0.346008 0.226074 0.103577 ]
0.489563
0.512695
0.540588
0.514282
0.366516
0.373474
0.399841
0.386169
0.240234
0.238037
0.264221
0.256165
0.109253 ]
0.108337 ]
0.118774 ]
.
0.117493 ]
p LSP ,6T = [ 1.037476 0.848816 0.684326 0.577393 0.463684 0.347717 0.232666 0.107239 ]
p LSP ,7T = [ 1.022278 0.809021 0.645081 0.535767 0.430481 0.325562 0.219055 0.099304 ]
p LSP ,8T = [ 0.964844 0.743469 0.578125 0.484375 0.393250 0.297913 0.201416 0.091736 ]
Adder 2164 calculates the prediction error vector
e 2 = e1 − ê1 ,
which is the input to the first-stage VQ. In block 2165 the 8-dimensional prediction error vector,
is
vector
quantized
with
the
128-entry,
8-dimensional
codebook,
e2 ,
{
}
, listed in Appendix 2. The codevector minimizing the MSE is
CB1 = cb1 , cb1 ,, cb1
~
denoted e21 and the corresponding index is denoted LSPI 1 :
(0)
(1)
(127 )
{(
k ∈{0 ,1,,127}
2
) (e
(k ) T
LSPI1 = arg min e 2 − cb1
− cb1
(k )
2
)},
At the first frame, the previous, non-existing, quantized interframe LSP prediction error vectors are set to zero-vectors.
- 14 -
( LSPI )
~
e21 = cb1 1 ,
where the notation I = arg min{D(i )} means that I is the argument that minimizes the entity
i
D(i ) , i.e.
D( I ) ≤ D(i ) for all i .
Adder 2166 subtracts the first-stage codevector from the prediction error vector to form the
quantization error vector of the first stage,
e 22 = e 2 − ~
e21 .
This is the input to the second-stage VQ, which is a sign-shape VQ with a 2-entry, 1-dimensional
sign codebook, S = {s0 , s1} = {− 1,+1}, and a 64-entry, 8-dimensional shape codebook,
{
}
, listed in Appendix 3. The product codevector that minimizes the
CB 2 = cb 2 , cb 2 ,, cb 2
WMSE, subject to the constraint that the 3 first elements of the intermediate quantized LSP vector,
(0)
(1)
( 63 )
l = ˆl + ~e2
,
= l + eˆ 1 + ~e21 + ~e22
preserve the ordering property
l1
l2
l3
≥
≥
≥
0
l1 ,
l2
is selected as,
(I )
~
e22 = sI sg cb 2 sh ,
where the indices are given by
{I sg , I sh } =
{
arg
min
{i , k }∈ {h , j } h∈{0 ,1}, l1( h , j ) ≥ 0 , l 2( h , j ) ≥ l1( h , j ) , l 3( h , j ) ≥ l 2( h , j ) , j∈{0 ,1,, 63}
{(e
}
and the weighting matrix is
w1
W=
0
w2
- 15 -
0
.
w8
22
− si cb 2
) W (e
(k ) T
22
− si cb 2
(k )
)},
( h, j )
The symbol li
is the i-th element of the reconstructed LSP vector l that is generated by using a
sign index I sg = h and the j-th shape codevector in CB 2 . From the sign index, I sg , and the shape
index, I sh , the index of the second stage VQ, LSPI 2 , is calculated as
127 − I sh , I sg = 0
LSPI 2 =
,
I sg = 1
I sh ,
In the unlikely event that no product codevector fulfills the constraint, the product codevector
(0)
~
e22 = cb 2 is selected, and the index LSPI 2 = 0 is returned.
Once the quantization is complete, the remaining operations of block 216 construct the quantized
LSP vector from the codevectors, LSP mean, and MA prediction. Adder 21611 calculates the
quantized prediction error vector by adding the stage 1 and stage 2 quantized vectors,
~
e2 = ~
e21 + ~
e22 .
Adder 21612 adds the mean LSP vector and the predicted mean-removed LSP vector to obtain the
predicted LSP vector,
ˆl = l + eˆ .
1
Adder 21613 adds the predicted LSP vector and the quantized prediction error vector to get the
intermediate reconstructed LSP vector,
l = ˆl + ~e2 .
Block 21614 checks the elements of the reconstructed LSP vector to enforce certain minimum
spacing rules. It enforces a minimum value of 6 Hz for the smallest LSP coefficient, a maximum
value of 3991 Hz for the largest LSP coefficient, and a minimum distance between neighboring
LSP coefficients of 50 Hz. In the normalized domain of the LSP coefficients, the spacing
requirement is given by
~
l1
≥ 0.0015
~
~
li +1 − li ≥ 0.0125 i = 1, 2,, 7 .
~
l8
≤ 0.99775
The spacing is carried out as follows:
(i)
The elements of the intermediate reconstructed LSP vector are sorted such that
l1 ≤ l2 ≤ ≤ l8 .
(ii)
Set
lmax = 0.91025 .
- 16 -
l1 < 0.0015 , set
else if l1 > lmax , set
~
else set l1 = l1 .
~
l1 = 0.0015 .
~
l1 = lmax .
(iii)
If
(iv)
for i = 2, 3, … , 8 do the following:
1.
2.
3.
3.5
~
lmin = li −1 + 0.0125 .
Set lmax ← lmax + 0.0125 .
~
If li < lmin , set li = lmin .
~
else if li > lmax , set li = lmax .
~
else set li = li .
Set
Conversion to Short-Term Predictor Coefficients
~
Refer back to Figure 5. In block 217, the quantized set of LSP coefficients { li }, which is
determined once a frame, is converted to the corresponding set of linear prediction coefficients
{ a~i }, the quantized linear prediction coefficients for the current frame.
With the notation
x p ,i
xm,i
~
= cos(π l2i −1 ), i = 1, 2, 3, 4
~
= cos(π l2i ), i = 1, 2, 3, 4
the 4 unique coefficients of each of the two polynomials Ap∆ ( z ) = Ap ( z ) /(1 + z −1 ) and
Am∆ ( z ) = Am ( z ) /(1 − z −1 ) can be determined using the following recursion:
For i = 1, 2, 3, 4, do the following :
(
a ∆p |m,i = 2 a ∆p |m,i − 2 − x p |m,i a ∆p |m,i −1
)
a ∆p |m, j = a ∆p |m, j + a ∆p |m, j − 2 − 2 x p |m,i a ∆p |m, j −1 , j = i − 1, i − 2,, 1
with initial conditions a ∆p|m,0 = 1 and a ∆p|m, −1 = 0 . In the recursion above, {a ∆p ,i } and {am∆ ,i } are the
sets of four unique coefficients of the polynomials Ap∆ (z ) and Am∆ (z ) , respectively. Similarly, let
the two sets of coefficients {a p ,i } and {am,i } , each of 4 unique coefficients except for a sign on
{am,i } , represent the unique coefficients of the polynomials Ap (z ) and Am (z ) , respectively. Then,
{a p ,i } and {am,i } can be obtained from {a ∆p ,i } and {am∆ ,i } as
a p ,i
am , i
= a ∆p ,i + a ∆p ,i −1 , i = 1, 2, 3, 4
= am∆ ,i − am∆ ,i −1 , i = 1, 2, 3, 4
- 17 -
From Ap (z ) and Am (z ) , the polynomial of the prediction error filter is obtained as
A p ( z ) + Am ( z )
~
.
A( z ) =
2
~
In terms of the unique coefficients of Ap (z ) and Am (z ) , the coefficients {a~i } of A( z ) can be
expressed as
1.0,
i=0
a~i = 0.5 ( a p ,i + am,i ),
i = 1, 2, 3, 4
0.5 ( a
i = 5, 6, 7, 8
p , 9 − i − am , 9 − i ),
where the tilde signifies that the coefficients correspond to the quantized LSP coefficients. Note
that
8
~
~ z −i ,
A( z ) = 1 − Ps ( z ) = 1 + ∑ a
i
i =1
where
8
Ps ( z ) = −∑ a~i z − i
i =1
is the transfer function of the short-term predictor block 240 in Figure 3.
Block 218 performs further bandwidth expansion on the set of predictor coefficients { a~i } using a
bandwidth expansion factor of γ 1 = 0.75. The resulting bandwidth-expanded set of filter
coefficients is given by
ai′ = γ 1 a~i , for i = 1, 2, …, 8.
i
This bandwidth-expanded set of filter coefficients { ai′ } is used to update the coefficients of the
weighted short-term synthesis filter block 221 in Figure 7 (to be discussed later). This completes
the description of short-term predictive analysis and quantization block 210 in Figure 3 and Figure
5.
3.6
Long-Term Linear Predictive Analysis (Pitch Extraction)
In Figure 3, the long-term predictive analysis and quantization block 220 uses the short-term
prediction residual signal d(n) of the current frame and its quantized version dq(n) in the previous
- 18 -
frames to determine the quantized values of the pitch period and the pitch predictor taps. This
block 220 is further expanded in Figure 7 below.
220
228
short-term
prediction
error
filter
221
223
222
weighted
d(n) short-term dw(n)
synthesis
filter
Low-pass
filter to
800 Hz
Decimate
to 2 kHz
sampling
rate
224
dwd(n)
First-stage
pitch period
search
cpp
225
s(n)
Second-stage
pitch period
search
PPI
ppt1
pp
227
Calculate
long-term
noise feedback
filter
coefficient
226
Pitch predictor
taps
quantizer
λ
PPTI
ppt
dq(n)
To blocks
230, 260 & 265
To block 265
To block
230 & 260
Figure 7 Long-term predictive analysis and quantization (block 220)
Now refer to Figure 7. Block 228 performs short-term prediction error filtering to get the shortterm prediction residual d(n) as follows.
8
d ( n ) = s( n ) + ∑ a~i s( n − i ) .
i =1
The short-term prediction residual signal d(n) passes through the weighted short-term synthesis
filter block 221, whose output is calculated as
8
dw( n ) = d ( n ) − ∑ ai′dw( n − i )
i =1
- 19 -
The signal dw(n) is passed through a fixed low-pass filter block 222, which has a –3 dB cut off
frequency at about 800 Hz. A 4th-order elliptic filter is used for this purpose. The transfer
function of this low-pass filter is
H lpf ( z ) =
0.0433083 − 0.0687180 z −1 + 0.0991097 z −2 − 0.0687180 z −3 + 0.0433083z −4
1 − 2.9580236 z −1 + 3.6337313z − 2 − 2.1249529 z −3 + 0.5003969 z − 4
Block 223 down-samples the low-pass filtered signal to a sampling rate of 2 kHz. This represents
an 4:1 decimation.
The first-stage pitch search block 224 then uses the decimated 2 kHz sampled signal dwd(n) to
find a “coarse pitch period”, denoted as cpp in Figure 7. The time lag represented by cpp is in
terms of number of samples in the 2 kHz down-sampled signal dwd(n). A pitch analysis window
of 15 ms is used. The end of the pitch analysis window is aligned with the end of the current
frame. At a sampling rate of 2 kHz, 15 ms correspond to 30 samples. Without loss of generality,
let the index range of n = 1 to n = 30 correspond to the pitch analysis window for dwd(n). Block
224 first calculates the following values
30
c( k ) = ∑ dwd ( n )dwd ( n − k ) ,
n =1
30
E ( k ) = ∑ [dwd ( n − k )] ,
2
n =1
c 2 ( k ), if c( k ) ≥ 0
c 2( k ) = 2
− c ( k ), if c( k ) < 0
for all integers from k = MINPPD - 1 to k = MAXPPD + 1, where MINPPD and MAXPPD are the
minimum and maximum pitch period in the decimated domain, respectively, MINPPD = 2 sample
and MAXPPD = 34 samples. Block 224 then searches through the range of k = MINPPD,
MINPPD + 1, MINPPD + 2, …, MAXPPD to find all local peaks 3 of the array { c 2( k ) / E ( k ) } for
which c(k) > 0. Let N p denote the number of such positive local peaks. Let k p ( j ) , j =1, 2, …,
N p be the indices where c 2( k p ( j )) / E ( k p ( j )) is a local peak and c( k p ( j )) > 0, and let
k p (1) < k p ( 2) < ... < k p ( N p ) . For convenience, the term c 2( k ) / E ( k ) will be referred to as the
“normalized correlation square”.
If N p = 0, the output coarse pitch period is set to cpp = MINPPD, and the processing of block 224
is terminated. If N p = 1, block 224 output is set to cpp = k p (1) , and the processing of block 224 is
terminated.
3
A value is characterized as a local peak if both of the adjacent values are smaller.
- 20 -
If there are two or more local peaks ( N p ≥ 2 ), then block 224 uses Algorithms 3.8.1, 3.8.2, 3.8.3,
and 3.8.4 (to be described below), in that order, to determine the output coarse pitch period cpp.
Variables calculated in the earlier algorithms will be carried over and used in the later algorithms.
Block 224 first uses Algorithm 3.8.1 below to identify the largest quadratically interpolated peak
around local peaks of the normalized correlation square c 2( k p ) / E ( k p ) . Quadratic interpolation is
performed for c( k p ) , while linear interpolation is performed for E ( k p ) . Such interpolation is
performed with the time resolution for the sampling rate of the input speech (8 kHz). In the
algorithm below, D denotes the decimation factor used when decimating dw(n) to dwd(n). Thus,
D = 4.
Algorithm 3.8.1 Find largest quadratically interpolated peak around c 2( k p ) / E ( k p ) :
(i) Set c2max = -1, Emax = 1, and jmax = 0.
(ii) For j =1, 2, …, N p , do the following 12 steps:
[
Set b = 0.5 [c( k
]
( j ) − 1)]
1. Set a = 0.5 c( k p ( j ) + 1) + c( k p ( j ) − 1) − c( k p ( j ))
2.
p
( j ) + 1) − c( k p
3. Set ji = 0
4. Set ei = E ( k p ( j ))
5. Set c 2m = c 2( k p ( j ))
6. Set Em = E ( k p ( j ))
7. If c 2( k p ( j ) + 1) E ( k p ( j ) − 1) > c 2( k p ( j ) − 1) E ( k p ( j ) + 1) , do the remaining part of step 7:
∆ = [ E ( k p ( j ) + 1) − ei ] D
For k = 1, 2, … , D/2, do the following indented part of step 7:
ci = a ( k / D ) 2 + b ( k / D ) + c( k p ( j ))
ei ← ei + ∆
If (ci ) 2 Em > (c 2m) ei , do the next three indented lines:
ji = k
c 2m = (ci ) 2
Em = ei
8. If c 2( k p ( j ) + 1) E ( k p ( j ) − 1) ≤ c 2( k p ( j ) − 1) E ( k p ( j ) + 1) , do the remaining part of step 8:
∆ = [ E ( k p ( j ) − 1) − ei ] D
For k = -1, -2, … , -D/2, do the following indented part of step 8:
ci = a ( k / D ) 2 + b ( k / D ) + c( k p ( j ))
ei ← ei + ∆
If (ci ) 2 Em > (c 2m) ei , do the next three indented lines:
ji = k
- 21 -
c 2m = (ci ) 2
Em = ei
9. Set lag ( j ) = k p ( j ) + ji / D
10. Set c 2i ( j ) = c 2m
11. Set Ei ( j ) = Em
12. If c2m × Emax > c2max × Em, do the following three indented lines:
jmax = j
c2max = c2m
Emax = Em
(iii) Set the first candidate for coarse pitch period as cpp = k p ( jmax ) .
The symbol ← indicates that the parameter on the left-hand side is being updated with the value
on the right-hand side 4.
To avoid picking a coarse pitch period that is around an integer multiple of the true coarse pitch
period, a search through the time lags corresponding to the local peaks of c 2( k p ) / E ( k p ) is
performed to see if any of such time lags is close enough to the output coarse pitch period of block
224 in the last frame, denoted as cpplast 5. If a time lag is within 25% of cpplast, it is considered
close enough. For all such time lags within 25% of cpplast, the corresponding quadratically
interpolated peak values of the normalized correlation square c 2( k p ) / E ( k p ) are compared, and
the interpolated time lag corresponding to the maximum normalized correlation square is selected
for further consideration. The following algorithm performs the task described above. The
interpolated arrays c2i( j) and Ei( j) calculated in Algorithm 3.8.1 above are used in this algorithm.
Algorithm 3.8.2 Find the time lag maximizing interpolated c 2( k p ) / E ( k p ) among all time lags
close to the output coarse pitch period of the last frame:
(i) Set index im = -1
(ii) Set c2m = -1
(iii) Set Em = 1
(iv) For j =1, 2, …, N p , do the following:
If | k p ( j ) − cpplast | ≤ 0.25 × cpplast , do the following:
If c 2i ( j ) × Em > c2m × Ei ( j ) , do the following three lines:
im = j
c 2m = c 2i ( j )
Em = Ei ( j )
4
5
An equal sign is not applicable due to a potential mathematical conflict.
For the first frame cpplast is initialized to 12.
- 22 -
Note that if there is no time lag k p ( j ) within 25% of cpplast, then the value of the index im will
remain at –1 after Algorithm 3.8.2 is performed. If there are one or more time lags within 25% of
cpplast, the index im corresponds to the largest normalized correlation square among such time
lags.
Next, block 224 determines whether an alternative time lag in the first half of the pitch range
should be chosen as the output coarse pitch period. Basically, block 224 searches through all
interpolated time lags lag( j) that are less than 16, and checks whether any of them has a large
enough local peak of normalized correlation square near every integer multiple of it (including
itself) up to 32. If there are one or more such time lags satisfying this condition, the smallest of
such qualified time lags is chosen as the output coarse pitch period of block 224.
Again, variables calculated in Algorithms 3.8.1 and 3.8.2 above carry their final values over to
Algorithm 3.8.3 below. In the following, the parameter MPDTH is 0.065, and the threshold array
MPTH(k) is given as MPTH(2) = 0.63, MPTH(3) = 0.48, MPTH(4) = 0.42, MPTH(5) = 0.36, and
MPTH(k) = 0.30, for k > 5.
Algorithm 3.8.3 Check whether an alternative time lag in the first half of the range of the coarse
pitch period should be chosen as the output coarse pitch period:
For j = 1, 2, 3, …, N p , in that order, do the following while lag( j) < 16:
(i) If j ≠ im, set threshold = 0.73; otherwise, set threshold = 0.4.
(ii) If c2i( j) × Emax ≤ threshold × c2max × Ei( j), disqualify this j, skip step (iii) for this j,
increment j by 1 and go back to step (i).
(iii) If c2i( j) × Emax > threshold × c2max × Ei( j), do the following:
a) For k = 2, 3, 4, … , do the following while k × lag( j) < 32:
1. s = k × lag( j)
2. a = (1 – MPDTH) s
3. b = (1 + MPDTH) s
4. Go through m = j+1, j+2, j+3, …, N p , in that order, and see if any of the
time lags lag(m) is between a and b. If none of them is between a and b,
disqualify this j, stop step (iii), increment j by 1 and go back to step (i). If
there is at least one such m that satisfies a < lag(m) ≤ b and c2i(m) × Emax
> MPTH(k) × c2max × Ei(m), then it is considered that a large enough peak
of the normalized correlation square is found in the neighborhood of the k-
- 23 -
th integer multiple of lag( j); in this case, stop step (iii) a) 4., increment k
by 1, and go back to step (iii) a) 1.
b) If step (iii) a) is completed without stopping prematurely, that is, if there is a large
enough interpolated peak of the normalized correlation square within
±100×MPDTH% of every integer multiple of lag( j) that is less than 32, then stop
this algorithm and stop the operation of block 224, and set cpp = k p ( j ) as the final
output coarse pitch period of block 224.
If Algorithm 3.8.3 above is completed without finding a qualified output coarse pitch period cpp,
then block 224 examines the largest local peak of the normalized correlation square around the
coarse pitch period of the last frame, found in Algorithm 3.8.2 above, and makes a final decision
on the output coarse pitch period cpp using the following algorithm. Algorithm 3.8.4 performs this
final decision. Again, variables calculated in Algorithms 3.8.1 and 3.8.2 above carry their final
values over to Algorithm 3.8.4 below. In the following, the parameters are SMDTH = 0.095 and
LPTH1= 0.79.
Algorithm 3.8.4: Final decision of the output coarse pitch period
(i) If im = -1, that is, if there is no large enough local peak of the normalized correlation square
around the coarse pitch period of the last frame, then use the cpp calculated at the end of
Algorithm 3.8.1 as the final output coarse pitch period of block 224, and exit this algorithm.
(ii) If im = jmax, that is, if the largest local peak of the normalized correlation square around the
coarse pitch period of the last frame is also the global maximum of all interpolated peaks of
the normalized correlation square within this frame, then use the cpp calculated at the end of
Algorithm 3.8.1 as the final output coarse pitch period of block 224, and exit this algorithm.
(iii) If im < jmax, do the following indented part:
If c2m × Emax > 0.43 × c2max × Em, do the following indented part of step (iii):
a) If lag(im) > MAXPPD/2, set block 224 output cpp = k p (im) and exit this
algorithm.
b) Otherwise, for k = 2, 3, 4, 5, do the following indented part:
1. s = lag(jmax) / k
2. a = (1 – SMDTH) s
3. b = (1 + SMDTH) s
- 24 -
4. If lag(im) > a and lag(im) < b, set block 224 output cpp = k p (im) and
exit this algorithm.
(iv) If im > jmax, do the following indented part:
If c2m × Emax > LPTH1 × c2max × Em, set block 224 output cpp = k p (im) and exit this
algorithm.
(v) If algorithm execution proceeds to here, none of the steps above have selected a final output
coarse pitch period. In this case, just accept the cpp calculated at the end of Algorithm 3.8.1
as the final output coarse pitch period of block 224.
Block 225 takes cpp as its input and performs a second-stage pitch period search in the
undecimated signal domain to get a refined pitch period pp. Block 225 first converts the coarse
pitch period cpp to the undecimated signal domain by multiplying it by the decimation factor D,
where D = 4. Then, it determines a search range for the refined pitch period around the value cpp
× D. The lower bound of the search range is lb = max(MINPP, cpp × D – D + 1) , where MINPP
= 10 samples is the minimum pitch period. The upper bound of the search range is ub =
min(MAXPP, cpp × D + D – 1), where MAXPP is the maximum pitch period, which is 136
samples.
Block 225 maintains a signal buffer with a total of MAXPP + 1 + FRSZ samples, where FRSZ is
the frame size, which is 40 samples. The last FRSZ samples of this buffer are populated with the
open-loop short-term prediction residual signal d(n) in the current frame. The first MAXPP + 1
samples are populated with the MAXPP + 1 samples of quantized version of d(n), denoted as
dq(n), immediately preceding the current frame. For convenience of writing equations later, the
symbol dq(n) will be used to denote the entire buffer of MAXPP + 1 + FRSZ samples, even
though the last FRSZ samples are really d(n) samples. Again, let the index range from n = 1 to n =
FRSZ denotes the samples in the current frame.
After the lower bound lb and upper bound ub of the pitch period search range are determined,
block 225 calculates the following correlation and energy terms in the undecimated dq(n) signal
domain for time lags k within the search range [lb, ub].
c~( k ) =
FRSZ
∑ dq(n)dq(n − k )
n =1
FRSZ
~
E ( k ) = ∑ dq( n − k ) 2
n =1
~
The time lag k ∈ [lb, ub] that maximizes the ratio c~ 2 (k ) / E (k ) is chosen as the final refined pitch
period. That is,
- 25 -
c~ 2 ( k )
pp = arg max ~
.
k ∈[ lb , ub ] E ( k )
Once the refined pitch period pp is determined, it is encoded into the corresponding output pitch
period index PPI, calculated as
PPI = pp − 10 .
Possible values of PPI are all integers from 0 to 126. Therefore, the refined pitch period pp is
encoded into 7 bits, without any distortion. The value of PPI = 127 is reserved for signaling
purposes and therefore is not used by the codec.
Block 225 also calculates ppt1, the optimal tap weight for a single-tap pitch predictor, as follows
c~( pp )
.
ppt1 = ~
E ( pp )
~
In the degenerate case where E ( pp ) = 0, ppt1 is set to zero. Block 227 calculates the long-term
noise feedback filter coefficient λ as follows.
ppt1 ≥ 1
0.5,
λ = 0.5 × ppt1, 0 < ppt1 < 1
0,
ppt1 ≤ 0
3.7
Long-Term Predictor Parameter Quantization
Pitch predictor taps quantizer block 226 quantizes the three pitch predictor taps to 5 bits using
vector quantization. The pitch predictor has a transfer function of
3
Pl ( z ) = ∑ bi z − pp + 2 − i ,
i =1
where pp is the pitch period calculated in Section 3.6.
Rather than minimizing the mean-square error of the three taps b1 , b2 , and b3 as in conventional
VQ codebook search, block 226 finds from the VQ codebook the set of candidate pitch predictor
taps that minimizes the pitch prediction residual energy in the current frame. Using the same dq(n)
buffer and time index convention as in block 225, and denoting the set of three taps corresponding
to the j-th codevector, b j = [b j1 b j 2 b j 3 ]T , as { b j1 , b j 2 , b j 3 }, we can express such pitch
prediction residual energy as
- 26 -
Ej =
FRSZ
∑
n =1
2
3
dq
(
n
)
b ji dq( n − pp + 2 − i )
−
∑
i =1
.
The codevector is selected from a 3-dimensional codebook of 32 codevectors, { b0 , b1 ,, b31} ,
listed in Appendix 4. The codevector that minimizes the pitch prediction residual energy is
selected. The index of the selected codevector is given by
PPTI = j* = arg min {E j }
j∈{0 ,1,, 31}
and the corresponding set of three quantized pitch predictor taps, denoted as ppt = {b1 , b2 , b3} in
Figure 7, is given by
b1
b = b .
j*
2
b3
This completes the description of block 220, long-term predictive analysis and quantization.
3.8
Excitation Gain Quantization
There is one residual gain for each frame. The unquantized residual gain is based on the pitch prediction residual of
the frame and is quantized in an open-loop fashion in the base-2 logarithmic domain. The quantization of the residual
gain is part of the prediction residual quantizer block 230 in Figure 3. Block 230 is further expanded in Figure 8. All
the operations in Figure 8 are performed on a frame-by-frame basis.
Block 300 in Figure 8 calculates the pitch prediction residual signal, given by
3
e( n ) = dq( n ) − ∑ bi dq( n − pp + 2 − i ), n = 1, 2, ..., FRSZ ,
i =1
where the same dq(n) buffer and time index convention of block 225 is used. That is, the current frame of dq(n) for n
= 1, 2, …, FRSZ is the unquantized open-loop short-term prediction residual signal d(n).
- 27 -
dq(n), ppt, pp
CI
GI
309
300
uq(n)
Estimate
signal
level
Calculate
pitch prediction
residual
lv(m -1)
e(n)
Residual
quantizer
codebook
search
Compare
with
threshold
Calculate
logarithmic
gain
303
+
+
mrlg(m)
+
+
-
305
lge(m)
311
307
lgeq(m)
-
Scale
residual
quantizer
codebook
gq(m)
306
Scalar
quantizer
312
313
310
301
lg(m)
230
+
308
+
lgq(m)
Convert to
linear gain
304
MA
log-gain
predictor
302
Log-gain
mean
value
elg(m)
lgmean
u(n)
Figure 8 Prediction residual quantizer (block 230)
Block 301 calculates the residual gain in the base-2 logarithmic domain. First, the average power
of the pitch prediction residual signal in the current frame, the m-th frame, is calculated as
Pe ( m) =
1
FRSZ
FRSZ
∑ e (n)
2
n =1
The logarithmic gain (log-gain) of the current frame is calculated as
log P ( m), if Pe ( m) > 1
lg ( m) = 2 e
.
0,
if Pe ( m) ≤ 1
The long-term mean value of the log-gain is calculated off-line and stored in block 302. This loggain mean value is lgmean = 11.45752. The adder 303 calculates the mean-removed version of the
log-gain as mrlg(m) = lg(m) - lgmean. The MA log-gain predictor block 304 is an 8th-order FIR
filter with its memory initialized to zero at the very first frame. The coefficients of this log-gain
predictor lgp(k), k = 1, 2, 3, …, 8, are fixed, as given below:
- 28 -
lgp(1) = 0.7801514
lgp(2) = 0.7377625
lgp(3) = 0.6150818
lgp(4) = 0.5926208
lgp(5) = 0.4674072
lgp(6) = 0.3635864
lgp(7) = 0.2378540
lgp(8) = 0.1286926
Block 304 calculates its output, the estimated log-gain, as
elg ( m) =
GPO
∑ lgp(k )lgeq(m − k ) ,
k =1
where GPO = 8 is the gain predictor order, and lgeq(m - k) is the quantized version of the log-gain
prediction error at frame m – k.
The adder 305 calculates the log-gain prediction error as
lge(m) = mrlg(m) - elg(m).
The scalar quantizer block 306 performs 4-bit scalar quantization of the resulting log-gain
prediction error lge(m). The codebook entries of this gain quantizer, along with the corresponding
codebook indices, are listed in Appendix 5. The operation of this quantizer is controlled by block
310, whose purpose is to achieve a good trade-off between clear-channel performance and noisychannel performance of the excitation gain quantizer. The operation of block 310 will be
described later.
For each temporarily quantized lgeq(m), the adders 307 and 308 together calculate the
corresponding temporarily quantized log-gain as
lgq(m) = lgeq(m) + elg(m) + lgmean
Block 309 estimates the signal level based on the final quantized log-gain, to be determined later
subject to the constraint imposed by block 310. Let lv(m) denote the output estimated signal level
of block 309 at frame m. Since the final value of lgq(m) has not been determined yet at this point,
block 310 can only use the estimated signal level at the last frame, namely, lv(m – 1). One way to
think of this situation is that block 309 has a one-sample delay unit for its input lgq(m).
At frame m, block 310 controls the quantization operation of block 306 based on lv(m – 1), lgq(m
– 1), and lgq(m – 2) 6. It uses an NG × NGC gain change threshold matrix T(i, j), i = 1, 2, …, NG, j
= 1, 2, …, NGC to limit how high lgq(m) can go. The parameter values are NG = 18 and NGC =
12. The threshold matrix T(i, j) is given in Appendix 6.
6
The initial values of lgq(m – 1) and lgq(m – 2) are 0, i.e. lgq(0)= 0 and lgq(-1)= 0.
- 29 -
Block 310 and block 306 work together to perform the quantization of lge(m) in the following
way. First, the row index into the threshold matrix T(i, j) is calculated as
lgq( m − 1) − lv ( m − 1) − GLB
i=
2
,
where GLB = –24, and the symbol . means “take the next larger integer” or “rounding to the
nearest integer toward infinity”. If i > NG, i is clipped to NG. If i < 1, i is clipped to 1.
Second, the column index into the threshold matrix T(i, j) is calculated as
lgq( m − 1) − lgq( m − 2) − GCLB
j=
2
,
where GCLB = –8. If j > NGC, j is clipped to NGC. If j < 1, j is clipped to 1.
Third, with the row and column indices i and j calculated above, a gain quantization limit is
calculated as
GL = lgq(m – 1) + T(i, j) – elg(m) – lgmean
Fourth, block 306 performs normal scalar quantization of lge(m) into its nearest neighbor in the
quantizer codebook. If the resulting quantized value is not greater than GL, this quantized value is
accepted as the final quantized log-gain prediction error lgeq(m), and the corresponding codebook
index is the output gain index GI m . On the other hand, if the quantized value is greater than GL,
the next smaller gain quantizer codebook entry is compared with GL. If it is not greater than GL, it
is accepted as the final output lgeq(m) of block 306, and the corresponding codebook index is
accepted as GI m . However, if it is still greater the GL, then block 306 keeps looking for the next
smaller quantizer codebook entry (in descending order of codebook entry value), until it finds one
that is not greater than GL. In such a search, the first one (that is, the largest one) that it finds to be
no greater than GL is chosen as the final output lgeq(m) of block 306, and the corresponding
codebook index is accepted as GI m . In the rare occasion when all the gain quantizer codebook
entries are greater than GL, then the smallest gain quantizer codebook entry is chosen as the final
output lgeq(m) of block 306, and the corresponding codebook index (0 in this case) is chosen as
the output GI m . The final gain quantizer codebook index GI m is passed to the bit multiplexer
block 295 of Figure 3.
Once the quantized log-gain prediction error lgeq(m) is determined in this way, adders 307 and
308 add elg(m) and lgmean to lgeq(m) to obtain the quantized log-gain lgq(m) as
lgq(m) = lgeq(m) + elg(m) + lgmean
- 30 -
After this final quantized log-gain lgq(m) subject to the constraint imposed by block 310 is
calculated, it is used by block 309 to update the estimated signal level lv(m). This value lv(m) is
used by block 310 in the next frame (the (m + 1)-th frame).
At frame m, after the final quantized log-gain lgq(m) is calculated, block 309 estimates the signal
level using the following algorithm. The parameter values used are α = 4095/4096, β = 511/512,
and γ = 255/256. At codec initialization, the related variables are initialized as: lmax(m - 1) = 100, lmin(m - 1) = 100, lmean(m - 1) = 12.5, lv(m - 1) = 17, and x(m - 1) =17.
Algorithm for updating estimated long-term average signal level:
(i) If lgq(m) > lmax(m - 1), set lmax(m) = lgq(m);
otherwise; set lmax(m) = lmean(m - 1) + α [lmax(m - 1) - lmean(m - 1)].
(ii) If lgq(m) < lmin(m - 1), set lmin(m) = lgq(m);
otherwise; set lmin(m) = lmean(m - 1) + α [lmin(m - 1) - lmean(m - 1)].
(iii) Set lmean(m) = β × lmean(m - 1) + (1 - β) [lmax(m) + lmin(m)]/2 .
(iv) Set lth = lmean(m) + 0.2 [lmax(m) – lmean(m)] .
(v) If lgq(m) > lth, set x(m) = γ × x(m - 1) + (1- γ)lgq(m), and set lv(m) = γ × lv(m - 1) + (1- γ)
x(m);
Otherwise, set x(m) = x(m - 1) and lv(m) = lv(m - 1).
Block 311 converts the quantized log-gain lgq(m) to the quantized gain gq(m) in the linear domain
as follows.
gq( m) = 2
lgq ( m )
2
Block 312 scales the residual vector quantization (also called excitation VQ) codebook by simply
multiplying every element of every codevector in the excitation VQ codebook by gq(m). The
resulting scaled codebook is then used by block 313 to perform Excitation VQ codebook search, as
described in the next section.
3.9
Excitation Vector Quantization
The excitation VQ codebook has a sign-shape structure, with 1 bit for sign and 4 bits for shape.
The vector dimension is 4. Thus, there are 16 independent shape codevectors stored in the
codebook, but the negated version of each shape codevector (i.e., the mirror image with respect to
the origin) is also a valid codevector for excitation VQ. The 16 shape codevectors, along with the
corresponding codebook indices, are listed in Appendix 7.
- 31 -
Block 313 in Figure 8 performs the excitation VQ codebook search using the filter structure shown
in Figure 9, which is essentially a subset of the encoder shown in Figure 3. The only difference is
that the prediction residual quantizer (block 230) in Figure 3 is replaced by block 248 in Figure 9,
which is labeled as “scaled VQ codebook”. This scaled VQ codebook is calculated in Section 3.8.
s(n)
+
+
-
255
v(n)
+
+
248
275
u(n)
Scaled
VQ
Codebook
-
270
+
dq(n)
+
sq(n)
+
sp(n)
280
-
+
ltnf(n)
245
285
uq(n)
240
Ps (z )
+
q(n)
265
N l ( z) − 1
260
ppv(n)
+
+
253
stnf(n)
290
-
+
250
Pl (z )
qs(n)
N s ( z) − 1
Figure 9 Filter structure used in excitation VQ codebook search
The four filters of blocks 240, 250, 260, and 265 have transfer functions given by
8
Ps ( z ) = −∑ a~i z − i (see Section 3.5),
i =1
where a~i is the i-th coefficient of the quantized short-term prediction error filter;
8
N s ( z) − 1 =
∑ βˆ
i
z −i
i =1
8
∑αˆ
(see Section 3.2);
i
z
−i
i =0
3
Pl ( z ) = ∑ bi z − pp + 2 − i ,
i =1
- 32 -
where pp is the pitch period, and bi is the i-th long-term predictor coefficient;
N l ( z ) − 1 = λ z − pp ,
where λ is the long-term noise feedback filter coefficient calculated in Section 3.6.
Using the filter structure in Figure 9, block 313 in Figure 8 performs excitation VQ codebook
search one excitation vector at a time. Each excitation vector contains four samples. The excitation
gain gq(m) is updated once a frame. Each frame contains 10 excitation vectors. Therefore, for each
frame, the same scaled VQ codebook is used in 10 separate VQ codebook searches corresponding
to the 10 excitation vectors in that frame.
Let n = 1, 2, 3, 4 denote the sample time indices corresponding to the current four–dimensional
excitation vector. Before the excitation VQ codebook search for the current excitation vector
starts, the high-pass filtered input s(n), n = 1, 2, 3, 4 has been calculated in Section 3.1. In
addition, before the VQ codebook search starts, the initial filter states (also called “filter memory”)
of the four filters in Figure 9 (blocks 240, 250, 260, and 265) are also known. All the other signals
in Figure 9 are not determined yet for n = 1, 2, 3, 4.
The basic ideas of the excitation VQ codebook search are explained below. Refer to Figure 9.
Block 248 stores the N scaled shape codevectors, where N = 16. Counting also the negated version
of each scaled shape codevector, it is equivalent to having 2N scaled codevectors available for
excitation VQ. From these 2N scaled codevectors, block 248 puts out one scaled codevector at a
time as uq(n), n = 1, 2, 3, 4. With the initial filter memories in blocks 240, 250, 260, and 265 set to
what were left after vector-quantizing the last excitation vector, this uq(n) vector then “drives” the
rest of the filter structure until the corresponding quantization error vector q(n), n = 1, 2, 3, 4 is
obtained. The energy of this q(n) vector is calculated and stored. This process is repeated for each
of the 2N scaled codevectors, with the filter memories reset to their initial values before the
process is repeated each time. After all 2N codevectors have been tried, the scaled codevector that
minimizes the energy of the quantization error vector q(n), n = 1, 2, 3, 4 is selected as the winning
scaled codevector and is used as the VQ output vector. The corresponding output VQ codebook
index is a 5-bit index consisting of a sign bit as the most significant bit (MSB), followed by 4
shape bits. If the winning scaled codevector is a negated version of a scaled shape codevector,
then the sign bit is 1, otherwise, the sign bit is 0. The 4 shape bits are simply the binary
representation of the codebook index of the winning shape codevector, as defined in Appendix 7.
Note that there are 10 such excitation codebook indices in a frame, since each frame has 10
excitation vectors. These 10 indices are grouped in an excitation codebook index array, denoted as
CI = {CI (1), CI ( 2),..., CI (10)} , where CI (k ) is the excitation codebook index for the k-th
excitation vector in the current frame. This excitation codebook index array CI is passed to the bit
multiplexer block 295 in Figure 3.
Given a uq(n) vector (taking the value of one of the 2N scaled codevectors), the way to derive the
corresponding energy of the q(n) vector is now described in more detail below. First, block 260
performs pitch prediction to produce the pitch-predicted vector ppv(n) as
- 33 -
3
ppv ( n ) = ∑ bi dq( n − pp + 2 − i ) , n = 1, 2, 3, 4.
i =1
Adder 285 then updates the dq(n) vector as
dq(n) = uq(n) + ppv(n) , n = 1, 2, 3, 4.
Next, block 240 and adder 245 together calculate short-term predicted speech vector sp(n) and
quantized speech vector sq(n) as follows.
For n = 1, 2, 3, 4, calculate sp(n) and sq(n) as follows:
8
sp( n ) = −∑ a~i sq( n − i )
i =1
sq( n ) = dq( n ) + sp( n )
Then, block 250 and adders 290, 253, and 255 work together to update the v(n) vector as follows.
For n = 1, 2, 3, 4, calculate stnf(n) and v(n) as follows:
8
8
i =1
i =1
stnf ( n ) = ∑ βˆi [v ( n − i ) − dq( n − i )] − ∑αˆi stnf ( n − i )
v ( n ) = s( n ) − sp( n ) − stnf ( n )
Finally, the corresponding q(n) vector is calculated as
q( n ) = v ( n ) − ppv ( n ) − λ q( n − pp ) − uq( n ) , n = 1, 2, 3, 4.
The energy of the q(n) vector is calculated as
4
Eq = ∑ q 2 ( n ) .
n =1
Such calculation from a given uq(n) vector to the corresponding energy term Eq is repeated 2N
times for the 2N scaled VQ codevectors. After the winning scaled codevector that minimizes
the Eq term is selected, the filter memories of blocks 240, 250, 260, and 265 are updated by using
the filter memories that were left after the calculation of the Eq term for that particular winning
codevector was done. Such updated filter memories become the initial filter memories used for
the excitation VQ codebook search for the next excitation vector.
- 34 -
3.10 Bit Multiplexing
The bit multiplexer block 295 in Figure 3 packs the five sets of indices LSPI, PPI, PPTI, GI, and
CI into a single bit stream. This bit stream is the output of the BraodVoice16 encoder. It is passed
to the communication channel.
Figure 10 shows the BV16 bit stream format in each frame. In Figure 10, the bit stream for the
current frame is the shaded area in the middle. The bit stream for the last frame is on the left, while
the bit stream for the next frame is on the right. Although the bit stream of different frames may
not be sent next to each other in a packet voice system, this illustration is meant to show that time
goes from left to right, and the 30 side information bits consisting of LSPI, PPI, PPTI, and GI goes
before the excitation codebook indices CI(k), k =1, 2, …, 10 when the bit stream is transmitted in a
serial manner. Note that for each index, the most significant bit (MSB) goes first (on the left),
while the least significant bit (LSB) goes last.
This completes the detailed description of the BV16 encoder.
PPI PPTI GI
7
7
0
7
5
5
4
5
...
5
30
7
7
7
5
80 bits
...
LSPI2
LSPI1
CI(1)
CI(2)
CI(3) ...
CI(10)
Frame M - 1
Frame M
Frame M + 1
(Previous Frame)
(Current Frame)
(Next Frame)
Figure 10 Bit stream format
- 35 -
4
4
DETAILED DESCRIPTION OF THE BV16 DECODER
This section gives a detailed description of each functional block in the BV16 decoder shown in
Figure 4. Those blocks or signals that have the same labels as their counterparts in the encoder of
Figure 3 have the same meaning as those counterparts.
4.1
Bit De-multiplexing
The bit de-multiplexer block 400 takes one frame of input bit stream at a time, and de-multiplexes,
or separates, the five sets of indices LSPI, PPI, PPTI, GI, and CI from the current frame of input
bit stream. As described in Section 3 above, LSPI contains two indices: a 7-bit first-stage VQ
index and a 7-bit second-stage VQ index. PPI is a 7-bit pitch period index. PPTI is a 5-bit pitch
predictor tap VQ index. GI is a 4-bit gain index, and CI contains ten 5-bit excitation VQ indices,
each with 1 sign bit and 4 shape bits.
4.2
Long-Term Predictor Parameter Decoding
The long-term predictor parameter decoder (block 410) decodes the indices PPI and PPTI. The
pitch period is decoded from PPI as
pp = PPI + 10
Let { b0 , b1 ,, b31} be the 3-dimensional, 32-entry codebook used for pitch predictor tap VQ, as
listed in Appendix 4. Let b j be the j-th codevector in this codebook, where the subscript j is the
codebook index listed in the first column of the table in Appendix 4. The three pitch predictor
taps b1 , b2 , and b3 are decoded from PPTI as
b1
b = b
PPTI .
2
b3
4.3
Short-Term Predictor Parameter Decoding
The short-term predictor parameter decoding takes place in block 420 of Figure 4. Block 420
receives the set of decoded LSP indices, LSPI = {LSPI 1 , LSPI 2 }, from the bit de-multiplexer,
~
block 400 in Figure 4. First, block 420 reconstructs the LSP coefficients, {li } , from the LSP
indices, and then it produces the coefficients of the short-term prediction error filter, {a~i } , from
the LSP coefficients according to the conversion procedure specified in Section 3.5.
- 36 -
4206
~e
2
8th order MA
prediction
42012
TEI
Index subquantizer 1
LSPI1
Index subquantizer 2
LSPI2
First stage VQ
4204
Mean LSP vector
~
e21
~e
42016
+
4208
(1)
l
42010
~e
22
+
+
(1)
2
Second stage VQ
4209
l̂
+
~e ( 2 )
2
4207
+
4205
Regular 8
dimensional
inverse subquantizer
Regular 8
dimensional
inverse subquantizer with
signed codebook
~e (1)
2
ê1
l
42014 42015
Buffer
( 2)
l
LP coefficients
LSP to LP
conversion
{a~i }
Check ordering
property of lower
3 LSF pairs
l
TEI
TEI
42013
~
l
LSP spacing
42011
Reconstructed
LSP vector
Figure 11 Short-term predictor parameter decoder (block 420)
Block 420 of Figure 4 is expanded in Figure 11. The reconstruction of the LSP coefficients from
the LSP indices is the inverse of the LSP quantization, and many operations have equivalents in
Section 3.4 and Figure 6. The first-stage VQ is decoded in block 4204, and the second-stage split
VQ is decoded in block 42016.
In block 42016, the received index LSPI 2 is decoded into the sign index,
0, LSPI 2 > 63
,
I sg =
1, LSPI 2 ≤ 63
and the shape vector index,
127 − LSPI 2 , LSPI 2 > 63
.
I sh =
LSPI 2 ≤ 63
LSPI 2 ,
From the sign and shape indices the reconstructed output of the second stage VQ is calculated as
(I )
~
e22 = sI sg cb 2 sh .
From the index for the first stage VQ, block 4204 looks up the quantized first stage vector from
(0)
(1)
(127 )
the codebook CB1 = cb1 , cb1 ,, cb1
,
( LSPI 1 )
~
.
e = cb
{
}
21
1
- 37 -
Adder 4205 performs the equivalent operation of Adder 21611 in Figure 6. It adds the first-stage
and second-stage vectors to obtain a first reconstructed prediction error vector,
~
e2(1) = ~
e21 + ~
e22 .
Equivalent to block 2163 in Figure 6, block 4206 performs the 8th-order MA prediction of the
mean-removed LSP vector according to
T
eˆ1,i = p LSP ,i [~
e2,i (1)
T
~
e2,i ( 2) ~
e2,i (3) ~
e2,i ( 4) ~
e2,i (5) ~
e2,i (6) ~
e2,i (7) ~
e2,i (8) ] , i = 1, 2,, 8 ,
where ~e2,i ( k ) and p LSP,i are defined in Section 3.4. Adder 4207, equivalent to Adder 21612 in
Figure 6, generates the predicted LSP vector by adding the mean LSP vector and the predicted
mean-removed LSP vector,
ˆl = l + eˆ .
1
Subsequently, adder 4208 adds the predicted LSP vector to the first reconstructed prediction error
vector to obtain a first intermediate reconstructed LSP vector,
(1)
l = ˆl + ~
e2(1) .
Adder 4209 subtracts the predicted LSP vector from a second intermediate reconstructed LSP l ( 2 ) ,
to calculate a second reconstructed prediction error vector
~
e2( 2 ) = l ( 2 ) − ˆl ,
to be used to update the MA predictor memory in the presence of bit-errors. Block 42010
determines the ordering property of the first 3 first intermediate reconstructed LSP coefficients,
l1(1)
l2(1)
l3(1)
≥
≥
≥
0
l1(1) ,
l2(1)
This ordering property was enforced during the encoding operation of the constrained VQ of the
second stage, block 21615 of Figure 6. If the ordering is found to be preserved, the TransmissionError-Indicator, TEI , is set to 0 to indicate that no bit-errors in the LSP bits have been detected.
Otherwise, if it is not preserved, the Transmission-Error-Indicator is set to 1 to indicate the likely
presence of bit-errors in the LSP bits.
If the Transmission-Error-Indicator is 0, the switches 42011 and 42012 are in the left position,
and they route the first reconstructed prediction error vector ~e2(1) and the first intermediate
- 38 -
reconstructed LSP vector l (1) to the reconstructed prediction error vector ~e2 and the intermediate
reconstructed LSP vector l , respectively. Otherwise, if the Transmission-Error-Indicator is 1, the
switches 42011 and 42012 are in the right position, and they route the second reconstructed
prediction error vector ~
e2( 2 ) and the second intermediate reconstructed LSP vector l ( 2 ) to the
reconstructed prediction error vector ~e2 and the intermediate reconstructed LSP vector l ,
respectively. Hence, the reconstructed prediction error vector and the intermediate reconstructed
LSP vector are obtained as
~
e2(1) , if TEI = 0
~
e2 = ~ ( 2 )
e2 , if TEI = 1
and
l (1) , if TEI = 0
,
l = ( 2)
l , if TEI = 1
respectively. Block 42013 enforces LSP spacing; it is functionally identical to block 21614 in
Figure 6, as specified in Section 3.4. Block 42014 buffers the reconstructed LSP vector for future
use in the presence of bit-errors. The reconstructed LSP vector of the current frame becomes the
second intermediate reconstructed LSP vector of the next frame,
( 2)
~
l ( k + 1) = l ( k ) ,
where the additional parameter k here represents the frame index of the current frame. For the
very first frame the second intermediate reconstructed LSP vector is initialized to
( 2)
T
l = [1 / 9 2 / 9 8 / 9]
The final step of the short-term predictor parameter decoding is to convert the reconstructed LSP
coefficients to linear prediction coefficients. This operation takes place in block 42015, which is
functionally identical to block 217 of Figure 5, described in Section 3.5.
4.4
Excitation Gain Decoding
The excitation gain decoder is shown in Figure 12. It is part of block 430 in Figure 4. It decodes
the gain index in GI into the corresponding decoded frame excitation gain gq(m) in the linear
domain. All operations in Figure 12 are performed on a frame-by-frame basis.
Refer to Figure 12. Let m be the frame index of the current frame, and assume the same
convention for the frame index m as in Section 3.8. Block 501 decodes the 4-bit gain index GI m
into the log-gain prediction error lgeq(m) using the codebook in Appendix 5. Switch 502 is
normally in the upper position, connecting the output of block 501 to the input of block 503.
- 39 -
Then, the MA log-gain predictor (block 503) calculates the estimated log-gain for the current
frame as
elg ( m) =
GPO
∑ lgp(k )lgeq(m − k ) ,
k =1
where GPO = 8, and lgp(k), k = 1, 2, …, GPO are the MA log-gain predictor coefficients given in Section 3.8.
507
Estimate
signal
level
lv(m -1)
508
Compare
with
threshold
501
GI
Gain
prediction
error
decoder
509
506
505
lgeq(m)
+
+
lgq'(m)
Determine
final
decoded
log-gain
510
lgq(m)
Convert
to
linear
gain
qg(m)
502
lgeq'(m)
-
503
MA
log-gain
predictor
511
512
+
+
+
-
+
elg(m)
lgmean
504
Log-gain
mean
value
Figure 12 Excitation gain decoder
Block 504 holds the long-term average log-gain value lgmean = 11.45752. Adders 505 and 506
adds elg(m) and lgmean, respectively, to lgeq(m), resulting in the temporarily decoded log-gain of
lgq′(m) = lgeq(m) + elg(m) + lgmean .
Block 507 is functionally identical to block 309 in Figure 8, described in Section 3.8. It is
important to note that equivalently to the encoder, the log-gain value passed to block 507 for
updating its estimate of the long-term average signal level is the final value of the decoded loggain lgq(m), i.e. after the threshold check of block 508 and potential log-gain extrapolation and
substitution of block 509, respectively, as described below.
- 40 -
Block 508 calculates the row and column indices i and j into the threshold matrix T(i, j) in the
same way as block 310 in Figure 8. Namely, the row index is calculated as
lgq( m − 1) − lv ( m − 1) − GLB
i=
2
,
where GLB = –24. If i > NG, i is clipped to NG. If i < 1, i is clipped to 1. The column index is
calculated as
lgq( m − 1) − lgq( m − 2) − GCLB
j=
2
,
where GCLB = –8. If j > NGC, j is clipped to NGC. If j < 1, j is clipped to 1.
Block 508 controls the actions of block 509 and switch 502 in the following way. If GI m = 0 or
lgq′(m) ≤ T(i, j) + lgq(m – 1), then switch 502 is in the upper position, block 509 determines the
final decoded log-gain as
lgq( m) = lgq′( m) ,
and the filter memory in the MA log-gain predictor (block 503) is updated by shifting the old
memory values by one position, and then assigning lgeq(m) to the newest position of the filter
memory.
If, on the other hand, GI m > 0 and lgq(m) > T(i, j) + lgq(m – 1), then the temporarily decoded loggain lgq′(m) is discarded, block 509 determines the final decoded log-gain as
lgq( m) = lgq( m − 1)
(by extrapolating the decoded log-gain of the last sub-frame); furthermore, switch 502 is moved to
the lower position, adders 511 and 512 subtract lgmean and elg(m), respectively, from lgq(m) to
get
lgeq′( m) = lgq( m) − lgmean − elg ( m) ,
and this lgeq′(m) is used to update the newest position of the filter memory of block 503, after the
old memory values are shifted by one position.
Once the final decoded log-gain lgq(m) subject to the constraint imposed by block 509 is
determined as described above, it is used by block 508 to update the estimated signal level lv(m).
This value lv(m) is then used by block 509 in the next frame (the (m + 1)-th frame).
- 41 -
Block 510 converts final decoded log-gain lgq(m) to the linear domain as gq( m) = 2
4.5
lgq ( m )
2
.
Excitation VQ Decoding and Scaling
The excitation codebook index array CI of each frame contains 10 excitation codebook indices,
CI(k), k = 1, …, 10, each containing 1 sign bit and 4 shape bits. The excitation vectors are
decoded vector-by-vector.
Let gq(m) denote the decoded excitation gain in the linear domain for the current frame. In
addition, let CI(k) denote the received excitation codebook index of the current excitation vector
that needs to be decoded. This index assumes a value between 0 and 31. The most significant bit
of this index is the sign bit. Therefore, if CI(k) < 16, the sign bit is 0; otherwise, the sign bit is 1.
Let c j (n ), n = 1, 2, 3, 4 represent the j-th shape codevector in Appendix 7, with a shape codebook
index of j. Furthermore, without loss of generality, let n = 1, 2, 3, 4 correspond to the sample time
indices of the current vector. Then, in Figure 4, the decoded and scaled excitation vector, or uq(n),
n = 1, 2, 3, 4, is obtained as
gq( m) cCI ( k ) ( n ), n = 1, 2, 3, 4,
uq( n ) =
− gq( m) cCI ( k ) −16 ( n ), n = 1, 2, 3, 4,
4.6
if CI ( k ) < 16
if CI ( k ) ≥ 16
Long-Term Synthesis Filtering
Let n = 1, 2, …, FRSZ correspond to the sample time indices of the current frame. In Figure 4, the
long-term synthesis filter (block 455, consisting of block 440 and adder 450 in a feedback loop)
performs sample-by-sample long-term synthesis filtering as follows.
3
dq( n ) = uq( n ) + ∑ bi dq( n − pp + 2 − i ) , n = 1, 2, … FRSZ.
i =1
4.7
Short-Term Synthesis Filtering
The short-term synthesis filter (block 475, consisting of block 460 and adder 470 in a feedback
loop) performs sample-by-sample short-term synthesis filtering to obtain the output signal as
follows.
8
sq( n ) = dq( n ) − ∑ a~i sq( n − i ) , n = 1, 2, … FRSZ.
i =1
- 42 -
4.8
Example Postfilter
This document specifies codec components that need to be clearly specified in order to foster interoperability. Decoder postfiltering is not a mandatory component of this BV16 Codec
Specification, since such postfiltering does not affect bit-stream compatibility or encoder-decoder
inter-operability. However, an example postfilter is described in this section for reference
purposes only. An implementer of BV16 can utilize other postfilters without affecting interoperability.
The example postfilter is an all-zero single tap pitch postfilter. The input to the pitch postfilter is
the pitch period, pp, and the output signal, sq(n), from the short-term synthesis filter 7. In principle,
the postfiltering is given by
spf (n) = b pf (1) sq (n) + b pf (2) sq (n − pppf ) , n = 1, 2, … FRSZ,
where spf(n) denotes the postfiltered output signal and pppf is the pitch period used for the pitch
postfilter.
First the pitch period of the decoder is refined by selecting the lag, pppf, corresponding to the
highest squared normalized pitch correlation of the output signal in a ±4 sample range of the pitch
period, pp, i.e. the lag, pppf, that maximizes,
2
FRSZ
∑ sq (n) sq (n − pppf )
n =1
, pppf = ppmin, ppmin+1, … , ppmax,
Csq ( pppf ) = FRSZ
FRSZ
∑ sq (n) sq (n) ∑ sq (n − pppf ) sq (n − pppf )
n =1
n =1
where ppmin = pp-4 and ppmax = pp+4, with the following constraints:
if ppmin < MINPP: ppmin = MINPP, ppmax = MINPP+8, and similarly
if ppmax > MAXPP: ppmax = MAXPP, ppmin = MAXPP-8.
With the refined lag the normalized pitch correlation is calculated as
Cpf =
7
FRSZ
∑ sq (n) sq (n − pppf )
n =1
FRSZ
FRSZ
∑ sq (n) sq (n) ∑ sq (n − pppf ) sq (n − pppf )
n =1
n =1
At the first frame, the history of sq(n) is set to zero.
- 43 -
If the numerator is less than zero or the denominator is zero, the normalized pitch correlation is set
to zero, Cpf = 0. Next, a running mean of the normalized pitch correlation is calculated as
Crm(m) = 0.75 Crm(m − 1) + 0.25 Cpf ,
where Crm(m) is the running mean of the current frame, and Crm(m-1) is the running mean of the
previous frame 8. Based on the normalized pitch correlation and the running mean of the
normalized pitch correlation, the initial pitch postfilter tap is calculated as
0
a pf =
0.3 Cpf
Crm(m) < 0.55 and Cpf < 0.8
otherwise
.
Subsequently, a scaling factor is calculated as
FRSZ
g pf =
∑ [sq(n)]
2
n =1
∑ [sq(n) + a
FRSZ
n =1
pf
sq (n − pppf )
]
.
2
It is set to one if either the numerator or the denominator is zero. The two pitch postfilter
coefficients of the current (m-th) frame is calculated as
b pf ,m (1) = g pf and b pf ,m (2) = g pf a pf .
In practice, for the first Lint=20 samples of each frame, the impulse responses of adjacent pitch
postfilters are interpolated while the pitch postfilter of the current frame is used for the remaining
samples of the frame:
spf (n) = b pf (1, n) sq (n) + b pf (2, n) sq (n − pppf m ) + b pf (3, n) sq (n − pppf m −1 ) , n = 1, 2, … FRSZ,
where pppfm and pppfm-1 are the refined pitch period of the current and previous frames,
respectively, and
α ( n ) bpf , m (1) + [1 − α ( n )] bpf , m −1 (1)
=
bpf , m (1)
α ( n ) bpf , m ( 2)
bpf ( 2, n ) =
bpf , m ( 2)
[1 − α ( n )] bpf , m −1 ( 2)
bpf (3, n ) =
0
bpf (1, n )
8
For the first frame, running mean of the previous frame is set to zero, i.e. Crm(0)=0.
- 44 -
n ≤ L int
n > L int
n ≤ L int
.
n > L int
n ≤ L int
n > L int
A linear interpolation between adjacent pitch postfilters 9 is used:
α ( n) =
4.9
n
.
L int + 1
Example Packet Loss Concealment
Similar to decoder postfiltering, packet loss concealment is not a mandatory component of this
BV16 Codec Specification, since packet lost concealment does not affect bit-stream compatibility
or encoder-decoder inter-operability. However, an example packet loss concealment technique is
described in this section for reference purposes only. An implementer of BV16 can utilize other
packet loss concealment techniques without affecting inter-operability.
The example packet loss concealment technique utilizes the synthesis model of the decoder. In
principle, all side information of the previous frame is repeated while the excitation of the
cascaded long-term and short-term synthesis filters is from a random source, scaled to a proper
level. Hence, with the additional index m denoting the m-th frame, during packet-loss:
•
•
•
The pitch period, pp, is set to the pitch period of the last frame 10:
pp = ppm −1 .
The pitch taps, b1 b2 and b3, are set to the pitch taps of the last frame 11.
bi = bm −1,i , i=1,2,3.
The short-term synthesis filter coefficients, a~ , i = 1,...,8 , are set to those of the last frame 12:
i
a~i = a~m −1,i , i=1,…,8.
•
A properly scaled random sequence is used as long-term synthesis filter excitation, uq(n), n
= 1, 2, … FRSZ.
The speech synthesis of the bad frame (part of lost packet) now takes place exactly as specified in
Sections 4.6, 4.7, and 4.8 if the example postfilter is included.
The random sequence is scaled according to
uq (n) = g plc ⋅
Em −1
FRSZ
∑ [r (n)]
⋅ r (n) , n = 1, 2, … FRSZ,
2
n =1
where r(n), n = 1, 2, … FRSZ, is a random sequence, Em-1 is in principle the energy of the longterm synthesis filter excitation of the previous frame 13, and the scaling factor, gplc, is calculated as
detailed below.
9
For the first frame, the parameters of the previous pitch postfilter are set to pppf0=100, b0(1)=1, b0(2)=0.
If the first frame is lost a value of 100 is used for the pitch period.
11
If the first frame is lost the pitch taps are set to zero.
12
If the first frame is lost the short-term filter coefficients are set to zero.
10
- 45 -
During good frames an estimate of periodicity is updated as
perm = 0.5 perm −1 + 0.5 bs ,
where bs is the sum of the three pitch taps clipped at a lower threshold of zero and an upper
threshold of one 14, while it is maintained during bad frames: perm = perm −1 . Based on the
periodicity the scaling factor is calculated as
g plc = −2 perm −1 + 1.9
with gplc clipped at a lower threshold of 0.1 and an upper threshold of 0.9.
After synthesis of the signal output of a lost frame, memories of predictive quantizers are updated.
The memory of the inverse LSP quantizer is updated with
~
e~2,i = I m −1,i − eˆ1,i − I i , i=1,2,…,8,
~
where eˆ1,i is given in Section 4.3, I i in Section 3.4, and I m −1,i denotes the i-th LSP coefficients of
the (m-1)-th frame (as decoded according to Section 4.3 for a good frame, or repeated for a bad
frame).
The memory of the inverse gain quantizer is updated with
lgeq(m) = lgq (m) − lgmean − elg (m) ,
where elg (m) is given in Section 4.4, lgmean in Section 3.8, and lgq(m) is calculated as
Em −1
Em −1
log 2 FRSZ , if FRSZ > 1
.
lgq (m) =
Em −1
0 ,
if
≤1
FRSZ
The level estimation for a bad frame is updated exactly as for a good frame, see Section 4.4.
At the end of a good frame (after synthesis of the output) the estimate of periodicity is estimated as
explained above, and the energy of the long-term synthesis filter excitation is updated as
Em =
13
14
FRSZ
∑ [uq(n)]
n =1
The energy is initialized to zero, i.e. E0=0.
The estimate of periodicity is initialized to zero, i.e. per0=0.
- 46 -
2
.
At the end of the processing of a bad frame (after synthesis of the output and update of predictive
quantizers), the energy of the long-term synthesis filter excitation and the long-term synthesis filter
coefficients are scaled down when 8 or more consecutive frames are lost:
E
Em = m −1 2
(β Nclf ) Em −1
bm −1,i
bm ,i =
β Nclf bm −1,i
Nclf < 8
,
Nclf ≥ 8
Nclf < 8
, i=1,2,3,
Nclf ≥ 8
where Nclf is the number of consecutive lost frames, and the scaling, β Nclf , is given by
1 − 0.02 ( Nclf − 7) 8 ≤ Nclf ≤ 57
.
Nclf > 57
0
β Nclf =
This will gradually mute the output signal when consecutive packets are lost for an extended
period of time.
- 47 -
APPENDIX 1: GRID FOR LPC TO LSP CONVERSION
Grid point
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
Grid value
0.9999390
0.9935608
0.9848633
0.9725342
0.9577942
0.9409180
0.9215393
0.8995972
0.8753662
0.8487854
0.8198242
0.7887573
0.7558899
0.7213440
0.6853943
0.6481323
0.6101379
0.5709839
0.5300903
0.4882507
0.4447632
0.3993530
0.3531189
0.3058167
0.2585754
0.2109680
0.1630859
0.1148682
0.0657349
0.0161438
-0.0335693
-0.0830994
-0.1319580
-0.1804199
-0.2279663
-0.2751465
-0.3224487
-0.3693237
-0.4155884
-0.4604187
-0.5034180
-0.5446472
-0.5848999
-0.6235962
-0.6612244
-0.6979980
-0.7336731
-0.7675781
-0.7998962
-0.8302002
-0.8584290
-0.8842468
-0.9077148
-0.9288635
-0.9472046
-0.9635010
-0.9772034
-0.9883118
-0.9955139
-0.9999390
- 48 -
APPENDIX 2: FIRST-STAGE LSP CODEBOOK
Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
Element 1
-0.0059814
-0.0053177
-0.0009308
0.0031128
-0.0023270
-0.0081329
0.0006104
0.0090561
-0.0044785
0.0042114
-0.0039902
0.0035553
0.0032806
0.0010910
-0.0047226
-0.0000229
-0.0074081
-0.0019760
-0.0044403
-0.0001907
-0.0057526
-0.0022125
-0.0016632
0.0046158
-0.0006714
0.0114136
0.0089951
0.0116730
0.0061035
0.0069580
0.0065002
0.0147324
-0.0083008
-0.0031662
-0.0050354
0.0078888
-0.0038910
-0.0030060
-0.0017471
0.0070190
-0.0084000
-0.0012970
-0.0008011
0.0257950
0.0024414
0.0045166
-0.0064392
-0.0008316
-0.0227127
0.0003204
-0.0022583
0.0022202
-0.0029678
0.0019226
0.0002441
0.0024185
-0.0076904
0.0066910
-0.0050049
0.0057602
0.0025711
Element 2
-0.0075378
-0.0019302
-0.0001831
0.0013046
-0.0014496
-0.0077362
0.0040817
0.0081329
-0.0070572
0.0052719
-0.0037308
0.0042877
0.0013351
0.0050964
-0.0046234
0.0000000
-0.0089569
-0.0027161
-0.0059509
0.0020599
-0.0060425
-0.0046158
0.0000076
0.0114517
-0.0006714
0.0131760
0.0114975
0.0303574
0.0072174
0.0107727
0.0056458
0.0161285
-0.0135345
-0.0056992
-0.0077744
0.0054169
-0.0082321
-0.0051117
-0.0031509
0.0111084
-0.0120621
-0.0023575
0.0027390
0.0224075
0.0011520
0.0086136
-0.0072556
0.0018845
-0.0309753
-0.0007782
-0.0034180
0.0045013
-0.0052338
0.0010529
0.0029984
0.0418625
-0.0126266
0.0088730
-0.0121231
0.0139847
0.0053101
Element 3
-0.0113449
0.0037079
-0.0040741
0.0076218
-0.0036392
0.0091782
-0.0010300
0.0096436
-0.0158615
-0.0061417
-0.0103226
0.0199356
-0.0004501
0.0128632
0.0096664
0.0265427
-0.0175552
-0.0077667
-0.0128784
0.0160294
-0.0029755
-0.0083923
-0.0051346
0.0148926
-0.0119095
0.0045929
-0.0246811
0.0396042
0.0028000
0.0140839
0.0067139
0.0276260
-0.0167313
-0.0011444
-0.0103531
0.0038223
-0.0112686
0.0013962
-0.0094299
0.0142746
-0.0198364
0.0041809
-0.0027847
0.0190277
-0.0020218
0.0284348
0.0081406
0.0423431
-0.0029831
0.0007553
-0.0074692
0.0243607
0.0025406
0.0046844
0.0021286
0.0316925
-0.0251846
0.0157623
-0.0460205
0.0579147
0.0119553
Element 4
-0.0002670
-0.0106049
-0.0110474
-0.0042191
0.0071030
0.0048294
-0.0081787
0.0009613
0.0019913
0.0057449
-0.0064774
0.0078812
0.0149384
0.0091553
0.0042496
0.0128021
-0.0174561
-0.0104675
-0.0197525
0.0045853
-0.0092010
0.0117264
-0.0084305
0.0092087
-0.0186539
-0.0096207
-0.0092545
0.0238495
-0.0075989
0.0036621
0.0007935
0.0238800
-0.0003433
0.0063324
-0.0145035
-0.0016632
0.0100861
0.0250015
-0.0154495
0.0070648
-0.0063629
0.0055084
-0.0278168
0.0123291
0.0018616
0.0160980
0.0079956
0.0217514
-0.0045471
-0.0061646
-0.0160370
0.0083466
0.0110321
0.0322113
0.0054932
0.0256805
-0.0261307
0.0102997
-0.0182266
0.0351944
0.0070419
- 49 -
Element 5
-0.0103607
-0.0021820
-0.0238800
-0.0073776
0.0026093
0.0101395
-0.0126343
0.0011063
0.0087204
0.0057068
-0.0049667
0.0031281
0.0076141
0.0088348
-0.0064697
0.0049896
-0.0057831
-0.0090866
-0.0304413
-0.0091476
0.0054550
0.0248260
-0.0128784
0.0188599
0.0112305
0.0138092
-0.0067444
0.0113144
0.0156174
0.0325394
0.0008087
0.0214386
-0.0090408
-0.0090790
-0.0191193
-0.0109177
-0.0043945
-0.0003738
-0.0188980
-0.0085373
0.0110550
0.0066299
-0.0051651
0.0018692
0.0149918
0.0127563
-0.0225372
0.0008698
-0.0044708
-0.0099792
-0.0401917
-0.0246048
0.0029221
0.0202255
-0.0150223
0.0141296
0.0040588
0.0193558
-0.0260468
-0.0040665
0.0170135
Element 6
-0.0055771
-0.0003815
-0.0042191
-0.0045013
-0.0172119
0.0007172
-0.0218582
-0.0042572
0.0005951
-0.0022202
-0.0043411
-0.0082245
0.0033264
0.0151443
-0.0039902
0.0054398
-0.0148010
-0.0027542
-0.0161133
-0.0058670
-0.0046692
0.0126343
-0.0196915
-0.0058212
-0.0053024
0.0076675
-0.0065155
0.0006714
0.0043716
0.0216980
-0.0099030
0.0131302
-0.0008469
0.0121918
-0.0035934
-0.0039520
-0.0049820
-0.0045395
-0.0264816
-0.0219345
0.0045700
0.0041122
-0.0065536
-0.0124512
0.0050735
0.0124054
-0.0159760
-0.0041199
-0.0003662
0.0272598
-0.0083847
0.0046997
-0.0056763
0.0150070
-0.0383453
0.0077591
0.0132675
0.0230255
-0.0259018
-0.0186386
0.0213165
Element 7
0.0091400
0.0100098
0.0014114
-0.0051651
-0.0009613
0.0030212
-0.0007629
0.0038910
0.0022583
0.0133896
-0.0066986
-0.0142746
-0.0038376
0.0096664
-0.0056915
0.0008698
0.0076141
0.0306244
0.0037613
0.0226593
-0.0137711
0.0082626
-0.0223007
0.0079727
0.0070267
0.0137863
-0.0055161
-0.0080719
-0.0073624
0.0056152
-0.0182724
0.0047607
-0.0017624
0.0022354
-0.0159454
-0.0170212
-0.0151062
0.0120697
-0.0149384
0.0042267
0.0082169
0.0141602
-0.0094833
-0.0261765
-0.0103073
0.0261307
-0.0059891
0.0085602
0.0006409
0.0179977
-0.0189896
-0.0021439
-0.0311356
0.0069733
-0.0137787
0.0154495
0.0196609
0.0201874
-0.0209122
-0.0284729
-0.0242462
Element 8
-0.0032730
0.0037460
-0.0061035
0.0158539
-0.0059662
0.0013885
-0.0092163
-0.0034485
-0.0074539
0.0077362
-0.0186844
-0.0015106
-0.0110245
0.0043411
-0.0162430
-0.0047150
0.0079803
0.0160751
0.0098114
0.0125122
-0.0035477
0.0001907
-0.0168076
0.0046082
-0.0016022
0.0142441
-0.0072098
0.0067749
-0.0141525
0.0061188
-0.0288086
-0.0047836
0.0161667
0.0048523
0.0042343
-0.0018616
0.0018616
0.0071411
0.0071030
0.0029221
0.0152664
0.0310822
-0.0070724
-0.0093994
0.0105972
0.0190277
-0.0012741
0.0102158
0.0024567
0.0155029
-0.0101929
0.0023041
-0.0081024
0.0021973
-0.0153046
0.0091095
0.0226059
0.0446930
-0.0175323
-0.0171432
-0.0078735
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
0.0176849
0.0018082
0.0040436
-0.0073166
0.0012283
-0.0025101
0.0082932
-0.0067825
-0.0006104
-0.0000534
0.0176697
-0.0064240
-0.0053406
-0.0037766
0.0014191
-0.0061264
-0.0086670
-0.0034256
-0.0020447
-0.0080795
0.0123291
-0.0029373
0.0141983
-0.0061722
-0.0051880
0.0019531
0.0283661
-0.0066528
0.0018539
-0.0135727
0.0099640
-0.0034714
-0.0011978
0.0023346
0.0088501
-0.0091782
-0.0042267
-0.0088577
0.0114822
-0.0047989
-0.0013351
-0.0027390
0.0343170
-0.0057678
-0.0029221
-0.0026627
0.0076294
-0.0038986
-0.0036087
0.0046082
0.0034485
-0.0314102
0.0007401
-0.0015106
0.0099411
-0.0020447
-0.0028152
0.0102158
0.0341263
-0.0082016
0.0022659
-0.0109406
0.0284500
-0.0009766
0.0068207
0.0341110
0.0054245
0.0186539
-0.0097580
0.0010452
-0.0054626
0.0077057
-0.0089111
-0.0049820
0.0012283
0.0149918
-0.0083618
-0.0044250
-0.0038528
0.0062637
-0.0116348
-0.0107346
-0.0035858
0.0006714
-0.0119095
0.0056229
-0.0080795
0.0176315
-0.0097046
-0.0111084
-0.0010757
0.0346222
-0.0088272
0.0035934
-0.0340042
0.0462646
-0.0054245
0.0003204
0.0034943
0.0262756
-0.0182266
-0.0044327
-0.0077744
0.0137863
-0.0082703
-0.0021439
-0.0010986
0.0352859
-0.0092545
-0.0022583
0.0002594
0.0422287
-0.0030670
0.0035782
0.0080490
0.0175858
-0.0573120
0.0014343
-0.0081024
0.0259781
-0.0056534
-0.0029221
0.0152206
0.0759430
-0.0157471
0.0080261
-0.0265427
0.0826721
-0.0013504
0.0242920
0.0360947
0.0223770
0.0682678
-0.0165558
-0.0012741
-0.0107498
0.0032272
-0.0184479
0.0121994
-0.0020981
0.0140686
-0.0160828
-0.0000076
-0.0154724
0.0232925
-0.0150681
0.0193634
-0.0015869
0.0414658
-0.0216980
-0.0075455
-0.0181351
0.0056763
-0.0205307
-0.0178680
-0.0006256
0.0130768
-0.0246811
-0.0006332
-0.0894012
0.0453796
-0.0114441
0.0082169
-0.0014572
0.0493164
-0.0430679
-0.0011826
-0.0206451
0.0051422
-0.0190659
0.0114136
-0.0103989
0.0218124
-0.0224991
0.0017624
-0.0224304
0.0109787
0.0042572
0.0424576
0.0111084
0.0821762
-0.0361557
0.0100021
-0.0302200
0.0389252
-0.0065460
0.0108337
-0.0010757
0.0464096
-0.0547714
0.0222321
-0.0845337
0.1126251
0.0082321
0.0580826
0.0325394
0.0096283
0.0692520
-0.0164719
-0.0147095
-0.0207672
-0.0084229
0.0017242
0.0176392
-0.0062180
0.0097809
-0.0105820
0.0004807
-0.0229874
0.0124817
0.0248032
0.0210648
-0.0019073
0.0249710
-0.0280533
-0.0211258
-0.0304947
-0.0044098
-0.0133286
0.0235138
-0.0082626
0.0101700
-0.0331345
-0.0124893
-0.0189590
0.0276489
0.0039444
0.0209274
0.0014343
0.0377655
0.0019302
0.0068283
-0.0217667
0.0017090
0.0137558
0.0434952
-0.0135803
0.0093842
-0.0150681
0.0089951
-0.0537415
0.0002823
0.0127869
0.0331345
-0.0065918
0.0272980
-0.0185471
0.0084229
-0.0461807
-0.0061798
-0.0004425
0.0616455
-0.0186920
0.0330963
-0.0575638
0.0229797
-0.0739746
0.0532761
0.0411072
0.0683975
- 50 -
0.0362167
-0.0214233
0.0290146
-0.0054932
0.0082169
-0.0277328
-0.0114975
-0.0064545
0.0069962
-0.0171661
0.0034790
0.0212021
0.0158310
0.0008011
0.0117035
0.0146561
0.0206528
-0.0142975
0.0197296
-0.0105896
-0.0128326
-0.0477219
-0.0164795
0.0058441
0.0195084
-0.0146942
0.0174866
0.0143738
0.0327225
-0.0093231
0.0160370
0.0178375
0.0536499
-0.0054932
0.0499496
0.0016556
0.0034714
-0.0166931
-0.0126801
-0.0056305
0.0037308
-0.0289612
-0.0054703
0.0306778
0.0164185
-0.0119095
0.0060196
0.0384750
0.0332794
-0.0519485
0.0156860
-0.0215912
0.0087585
-0.0730972
-0.0111618
-0.0104218
0.0292282
-0.0433731
0.0261612
0.0062866
0.0417252
-0.0471725
0.0114975
0.0223083
0.0789490
0.0317612
-0.0161209
0.0145493
0.0104904
0.0179520
0.0124207
0.0151215
-0.0217209
0.0066605
-0.0240707
0.0105209
0.0123367
0.0097198
0.0042114
0.0014648
0.0051270
0.0170822
0.0131302
0.0175705
-0.0002365
0.0001068
0.0010223
0.0085678
-0.0068512
0.0178680
-0.0227509
0.0197144
0.0057602
0.0176163
-0.0084381
0.0025406
0.0054550
0.0358963
-0.0127716
0.0279236
0.0016785
0.0279465
-0.0166321
-0.0010223
-0.0116806
-0.0042496
-0.0440826
-0.0086594
0.0213089
0.0172653
-0.0128479
0.0020523
0.0027542
0.0281830
-0.0157547
0.0159531
-0.0093765
0.0477676
-0.0303497
0.0061264
-0.0178070
0.0216827
-0.0584030
0.0209045
0.0029297
0.0456924
-0.0384445
-0.0184174
0.0020828
0.0621414
0.0233765
-0.0263824
0.0086975
0.0003052
0.0043182
0.0075836
0.0005341
-0.0024490
-0.0074310
0.0151367
0.0014572
-0.0018921
0.0251846
-0.0171432
-0.0106812
-0.0047836
0.0147781
-0.0151138
-0.0016098
-0.0009079
0.0061417
0.0070724
0.0159912
-0.0228195
0.0160370
-0.0364304
0.0173874
-0.0002747
0.0233994
-0.0090332
-0.0106049
-0.0171051
0.0197830
-0.0328522
-0.0018158
-0.0005188
0.0020370
-0.0041122
-0.0035172
-0.0150452
-0.0063400
0.0028915
0.0062637
0.0150299
0.0484390
-0.0069809
-0.0027847
-0.0117798
0.0280609
-0.0019455
0.0184174
-0.0097656
0.0178299
-0.0201721
0.0084991
-0.0585556
0.0112686
-0.0330887
0.0158920
0.0097733
0.0649796
-0.0217361
-0.0278168
-0.0291138
0.0445786
0.0178757
-0.0237961
0.0001144
-0.0026093
0.0050583
0.0025177
-0.0019226
-0.0019455
0.0169830
0.0081100
0.0027390
-0.0081329
0.0064545
-0.0225220
-0.0250015
-0.0073013
0.0120239
-0.0197067
0.0003967
0.0161667
0.0149689
0.0156937
0.0168228
0.0043335
0.0151443
0.0097427
0.0145874
-0.0007629
0.0193710
-0.0088577
-0.0192184
-0.0267639
0.0092850
-0.0487366
-0.0138168
-0.0010605
-0.0107651
-0.0111160
-0.0167770
-0.0211258
-0.0076370
0.0004730
0.0050507
0.0065155
0.0271988
-0.0067749
-0.0075226
-0.0054169
0.0207291
-0.0029144
0.0143356
-0.0018997
0.0107193
-0.0060349
-0.0016098
-0.0127106
0.0042725
-0.0182495
0.0113602
0.0060959
0.0428009
-0.0125504
-0.0385132
-0.0363312
0.0190887
126
127
0.0293198
0.0271988
0.0630722
0.0838776
0.0497131
0.1353760
-0.0120468
0.1022873
- 51 -
-0.0440521
0.0741501
-0.0440979
0.0458984
-0.0299225
0.0275192
-0.0291214
-0.0002823
APPENDIX 3: SECOND-STAGE LSP SHAPE CODEBOOK
Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
Element 1
-0.00045776
-0.00029755
-0.00118256
-0.00337219
0.00021362
-0.00189972
-0.00256348
-0.00487518
-0.00010681
-0.00064850
-0.00432587
-0.01011658
-0.00463867
-0.00424957
-0.00263214
0.00451660
0.00000763
-0.00207520
-0.00079346
-0.00298309
0.00035858
-0.00503540
-0.00057220
-0.00719452
-0.00290680
-0.00224304
-0.00428772
-0.01528931
-0.00177765
-0.00414276
-0.00677490
-0.00891113
-0.00096130
-0.00326538
-0.00380707
-0.00752258
0.00000763
-0.00630951
-0.00095367
-0.00719452
-0.00801086
-0.00186920
-0.00836945
-0.01739502
-0.00631714
-0.00720215
-0.00552368
0.00701904
0.00125885
-0.00152588
-0.00518036
-0.00656891
0.00264740
-0.00373077
-0.00197601
-0.01738739
-0.00734711
-0.00354767
-0.00668335
-0.03459930
-0.00075531
-0.00887299
Element 2
0.00002289
-0.00101471
-0.00199127
-0.00188446
0.00082397
-0.00291443
-0.00302124
-0.00572205
-0.00145721
-0.00139618
-0.00485229
0.00389099
-0.00547791
-0.00586700
-0.00723267
-0.01326752
0.00039673
-0.00434113
-0.00209808
-0.00412750
0.00170898
-0.00462341
-0.00221252
-0.01031494
-0.00582886
-0.00450134
-0.00616455
-0.00518799
-0.00195313
-0.00515747
-0.01074219
-0.01766968
-0.00098419
-0.00333405
-0.00453949
-0.00469208
-0.00075531
-0.00655365
-0.00313568
-0.01290894
-0.00989532
-0.00344086
-0.00853729
0.00542450
-0.01346588
-0.01295471
-0.01621246
-0.02751923
0.00099945
-0.00276184
-0.00488281
-0.00149536
0.00337219
-0.00283813
-0.01000214
-0.02180481
-0.00748444
-0.00787354
-0.00952911
0.00503540
-0.00411224
-0.02392578
Element 3
0.00099182
-0.00086212
-0.00380707
0.00494385
-0.00271606
-0.00379944
-0.00202942
0.00082397
-0.00476074
-0.00781250
-0.00765228
-0.00176239
-0.00512695
-0.00111389
-0.00938416
-0.00163269
0.00560760
0.00380707
0.00601196
0.01414490
0.00259399
0.00434875
-0.00485992
0.00247192
0.00511169
0.00026703
0.00080872
0.00449371
-0.00070953
-0.00126648
-0.01129150
0.00561523
-0.00413513
0.00357056
-0.00596619
0.00773621
-0.00958252
-0.00659943
-0.00857544
0.00675201
-0.00411224
-0.00266266
-0.00543976
-0.00310516
-0.01500702
-0.00642395
-0.01331329
0.00681305
0.00302887
0.01119232
0.00891876
0.02567291
-0.00964355
0.00484467
-0.00423431
0.00868988
0.01610565
-0.00128937
0.00102997
0.00631714
-0.02098846
-0.01149750
Element 4
0.00270081
0.00087738
-0.00403595
-0.00525665
-0.00733185
-0.00436401
0.00238037
0.00188446
0.00331116
-0.00374603
0.00385284
0.00331116
-0.00231171
0.00374603
0.00535583
-0.00040436
-0.00251770
0.00775909
-0.00038147
0.00212097
-0.01609802
-0.00257874
-0.00782013
-0.00617218
0.00539398
0.00598907
0.00531769
0.00463104
-0.01091003
0.00601959
-0.00234985
0.00578308
0.00173950
-0.00922394
-0.00867462
-0.00899506
-0.00546265
0.00173950
0.00354767
0.00386047
-0.00192261
-0.01030731
0.00074005
-0.00865936
-0.00602722
-0.00480652
0.01739502
0.00177765
-0.00566864
0.00526428
-0.00863647
-0.01044464
-0.03454590
-0.00320435
-0.00650787
-0.01496887
0.00942993
0.00877380
0.00148010
0.00146484
-0.01145172
0.03517151
- 52 -
Element 5
0.00746155
-0.00106049
-0.00030518
0.00161743
0.00150299
-0.00100708
-0.00732422
-0.00714111
0.01039886
0.00415039
0.00276184
0.00072479
0.00556183
0.00692749
-0.00386810
0.00058746
0.00186157
-0.00274658
-0.00785828
-0.00019073
-0.00000763
0.00315094
-0.00888824
-0.00236511
0.00604248
0.00528717
-0.00407410
0.00074005
0.01485443
0.01933289
-0.00765991
0.00490570
0.00495911
0.00468445
-0.00395966
0.00354004
-0.00563049
-0.00452423
-0.01430511
-0.01235962
0.01290894
0.00197601
0.00202942
-0.00068665
0.00646210
0.01066589
-0.00738525
-0.00151062
-0.00022125
-0.00367737
-0.00762939
-0.00462341
0.00086975
0.00572968
-0.02683258
-0.00506592
0.01790619
-0.00132751
-0.00872040
0.00110626
0.03794098
0.02966309
Element 6
0.00529480
0.00087738
0.00240326
-0.00501251
0.00543213
-0.00173187
0.00712585
-0.00489044
-0.00167847
-0.00397491
-0.00205231
-0.00367737
0.00563049
-0.00564575
0.00198364
-0.00355530
0.01089478
0.00917053
0.00248718
-0.00061798
0.00162506
0.00467682
0.00464630
0.00193024
0.00421143
0.00506592
-0.00506592
0.00062561
0.00244141
0.00696564
-0.00885773
0.00194550
0.01183319
0.00469971
-0.00065613
-0.01000214
0.01355743
0.00329590
0.00208282
-0.00559235
-0.00526428
-0.00857544
-0.00057983
0.00004578
0.01339722
-0.01364136
0.00836182
-0.00057220
0.02838135
0.01557159
0.00898743
-0.00055695
0.01454163
0.00675201
0.01259613
-0.00038147
0.01776123
0.00061798
0.00305176
-0.00109100
0.02877808
0.00566864
Element 7
-0.00106049
-0.01163483
0.00474548
0.00176239
0.00144958
-0.00433350
0.00381470
-0.00206757
0.00269318
-0.00685883
0.00595856
-0.00161743
-0.00107574
-0.00616455
-0.00283813
-0.00116730
-0.00137329
-0.00515747
-0.00209808
-0.00274658
-0.00133514
0.00090790
-0.00598145
-0.00309753
0.00669861
-0.00660706
0.00903320
0.00030518
0.00580597
-0.00712585
0.00039673
-0.00356293
0.00845337
-0.01351166
0.00699615
0.00416565
0.00136566
-0.00886536
0.00795746
0.00556183
0.00534058
-0.01191711
0.01557922
0.00126648
-0.00647736
-0.01339722
0.00555420
0.00139618
-0.00177765
-0.01921844
0.00974274
-0.00331879
0.00080109
-0.00530243
-0.00359344
-0.00035858
0.01367950
-0.02446747
0.03495026
-0.00064850
0.01161194
-0.02129364
Element 8
-0.00178528
0.00027466
0.00889587
0.00527191
-0.00333405
-0.00628662
0.00191498
-0.00262451
0.00548553
0.00500488
-0.00077057
-0.00171661
0.00090027
-0.00193024
0.00453949
-0.00093842
0.00457001
-0.00215149
0.01184082
0.00065613
0.00100708
-0.01029968
0.00544739
0.00254059
0.00392151
0.01150513
0.00828552
0.00215912
-0.00041199
-0.00523376
0.00651550
-0.00082397
-0.00122833
-0.00124359
-0.00128174
-0.00489044
-0.01033783
-0.01154327
-0.00600433
-0.00572968
0.01023865
0.01605988
-0.00852203
0.00104523
-0.00049591
-0.00752258
0.00202942
0.00026703
0.00128937
-0.00600433
0.00371552
-0.00172424
0.00027466
-0.03117371
0.00352478
0.00020599
0.01322937
0.01464081
0.01872253
0.00020599
0.00054932
-0.02059937
62
63
-0.01180267
-0.02636719
-0.03713226
-0.04943848
-0.03850555
0.01272583
-0.00773621
0.01393127
- 53 -
-0.01717377
0.00457001
-0.01065826
0.00045776
-0.00489044
0.00072479
-0.00129700
0.00040436
APPENDIX 4: PITCH PREDICTOR TAB CODEBOOK
Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Element 1
-0.0112610
-0.2130735
0.1685180
0.1311035
0.2671205
0.2727660
0.1418760
0.4192810
-0.0790405
0.0102235
0.1182555
0.0584410
0.2677000
0.0510560
0.2883300
0.4607545
0.0267945
-0.1670530
0.0991210
0.0505675
0.0785520
-0.0176085
0.2232055
0.2735290
-0.0867615
-0.2120360
-0.1777955
-0.1222230
0.1735230
-0.0948180
0.3175050
0.0957640
Element 2
-0.1767275
0.4948120
-0.5930480
0.2366640
0.3766175
0.8498230
0.2827150
0.5174865
0.2878420
0.5396730
-0.3234560
0.4372560
0.6710510
0.9555970
0.4584655
0.6708375
0.1656190
0.6494750
-0.2931520
0.3623655
0.4996340
0.7084045
0.3234255
0.4712525
0.2794190
0.8054810
-0.3514100
0.4702455
0.5760195
0.9347840
0.4074400
0.7896425
Element 3
0.0010985
0.0315245
0.2047120
0.1258850
0.0750425
-0.1647035
-0.0781860
0.0445555
-0.0986025
-0.2112120
-0.1257935
0.0544435
-0.0020140
-0.0752870
-0.1362610
-0.1777040
0.0300600
0.4698790
0.1622010
0.2523805
0.4010620
0.2586975
0.2569580
0.2355345
0.1318055
0.3201295
0.0110170
0.2991335
0.1928100
0.1232910
0.2554320
0.0609740
- 54 -
APPENDIX 5: GAIN CODEBOOK
Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Element
-5.38477
-3.68066
-2.76855
-2.09717
-1.47217
-0.33984
0.67285
1.82031
-0.88525
0.16748
1.20313
2.62549
3.80518
5.64551
8.70605
11.85156
- 55 -
Relative log-gain of previous frame, [dB2]
APPENDIX 6: GAIN CHANGE THRESHOLD MATRIX
-24 to –22
-22 to –20
-20 to –18
-18 to –16
-16 to –14
-14 to –12
-12 to –10
-10 to –8
-8 to –6
-6 to –4
-4 to –2
-2 to 0
0 to 2
2 to 4
4 to 6
6 to 8
8 to 10
10 to 12
i=1
i=2
i=3
i=4
i=5
i=6
i=7
i=8
i=9
i=10
i=11
i=12
i=13
i=14
i=15
i=16
i=17
i=18
-8 to -6
j=1
0.00000
0.00000
-1.96094
-1.96094
-1.47266
4.60547
10.66016
6.59375
2.64063
6.24805
6.18945
4.40430
3.39648
0.00000
0.00000
0.00000
0.00000
0.00000
-6 to -4
j=2
0.79102
13.85156
8.91211
8.66992
9.29297
12.33398
10.72656
10.19531
9.52539
8.26758
6.71875
5.46484
5.41602
3.50000
1.10156
-0.11914
0.00000
0.00000
-4 to -2
j=3
0.55664
1.73047
7.83594
13.53125
13.92578
14.09180
13.83203
13.34375
9.85547
8.78125
7.98438
6.17773
5.40039
4.60352
3.04492
-1.13672
0.00000
0.00000
-2 to 0
j=4
14.26563
13.76758
14.09961
14.09570
13.89063
14.14258
13.68359
12.87305
10.35938
9.08594
7.37109
6.04492
4.77734
3.92188
3.18945
1.41602
1.36861
0.52843
Log-gain change of previous frame, [dB2]
0 to 2
2 to 4
4 to 6
6 to 8
j=5
j=6
j=7
j=8
14.08398
0.00000
0.00000
0.00000
13.92773
0.00000
0.00000
0.00000
13.77930
0.91016
-2.41406
0.00000
13.95117
12.97461
2.14648
0.00000
13.87891
13.93750
12.20703
-4.99023
14.16016
13.48633
12.39063
2.01172
13.93945
13.77930
13.09570
10.17578
13.36719
13.36328
13.12891
12.66797
10.63086
12.92383
12.70508
12.65234
9.03125
10.34180
11.21875
11.07227
7.50391
7.69922
9.09180
8.73633
6.14063
6.84766
5.89063
5.43750
4.59375
4.63477
6.43359
3.54102
3.68164
4.21680
4.18750
3.32617
2.60156
2.43164
2.91016
1.48438
1.49609
0.72852
0.60352
-0.35352
1.18557
-0.36990
-4.01682
-2.21214
0.43190
0.00000
0.00000
-2.86324
- 56 -
8 to 10
j=9
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
-0.15430
0.72852
8.96680
8.32617
6.91211
4.67188
4.37891
3.38867
0.43555
-0.98242
0.00000
0.00000
10 to 12
j=10
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
-2.92578
0.30078
1.32422
8.41992
7.68750
5.58008
3.70117
2.32813
0.44336
-1.15039
-1.33077
0.00000
12 to 14
j=11
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
4.87109
4.86719
7.70313
7.22266
7.70898
6.64844
5.15039
1.50391
-1.99414
-3.04360
0.00000
14 to 16
j=12
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
7.85742
7.81445
7.86133
3.50977
7.46094
4.74414
1.76563
1.75391
0.00000
0.00000
0.00000
APPENDIX 7: EXCITATION VQ SHAPE CODEBOOK
Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Element 1
-0.514526
0.389648
-0.263916
2.927368
-0.348755
1.912231
-0.180298
1.743286
-1.407593
2.324219
-0.330933
1.545532
-0.583740
0.587769
-1.211426
0.196289
Element 2
0.847412
1.125000
-0.053101
-0.262695
-0.356812
0.890869
-1.221802
-1.338379
1.109497
1.637939
-0.405396
-0.195068
0.456055
-0.129028
-0.743896
-1.870728
Element 3
0.166748
-1.070557
0.189209
-0.092896
-0.765747
-2.045654
-1.728760
0.184204
1.724487
0.742188
0.890747
0.148560
0.253296
0.616699
-0.608887
-0.309326
Element 4
0.120605
0.048584
0.177734
0.274292
-0.639038
-0.802124
-0.965210
-0.281128
-0.347900
0.526001
1.477661
0.073486
-1.269043
-0.256714
-0.219360
1.111694
- 57 -