Presentation

Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Recent Advances in Speech Dereverberation
Dr.Ir. Emanuël Habets
In collaboration with Dr. Sharon Gannot and Dr. Israel Cohen
Department of Electrical Engineering, Technion - IIT
School of Electrical Engineering, Bar-Ilan University
IBM Speech Technologies Seminar 2008
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Outline
1
Introduction
2
Existing Speech Dereverberation Techniques
3
Proposed Speech Dereverberation Technique
4
Experimental Results
5
Summary and Future Research
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
What is reverberation?
Motivation for Speech Dereverberation
Applications
Problem Formulation
Challenges
Outline
1
Introduction
2
Existing Speech Dereverberation Techniques
3
Proposed Speech Dereverberation Technique
4
Experimental Results
5
Summary and Future Research
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
What is reverberation?
Motivation for Speech Dereverberation
Applications
Problem Formulation
Challenges
What is reverberation?
Reverberation is the process of multi-path propagation
of a sound from its source to a receiver.
Audio Example:
Anechoic Speech.
Reverberation Speech.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
What is reverberation?
Motivation for Speech Dereverberation
Applications
Problem Formulation
Challenges
Motivation for Speech Dereverberation
Wall
Interferences
Desired Source
Microphone signal
Microphone
Signal degradation that is caused by reverberation and ambient
noise can decrease the fidelity and intelligibility of speech and the
recognition performance of automatic speech recognition systems.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
What is reverberation?
Motivation for Speech Dereverberation
Applications
Problem Formulation
Challenges
Motivation for Speech Dereverberation
Desire to work hands-free and handset-free !!!
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
What is reverberation?
Motivation for Speech Dereverberation
Applications
Problem Formulation
Challenges
Applications
There is a variety of applications for speech dereverberation.
Automotive Hands-Free Car Phone Kits.
Health Hearing Aids, Home-Care.
Home/Office Speech and Speaker Recognition, Internet Telephony,
Teleconferencing, Set-top boxes, Home Automation.
Mobile Mobile Phones, Smartphones, PDA’s, Mobile
Multimedia Systems.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
What is reverberation?
Motivation for Speech Dereverberation
Applications
Problem Formulation
Challenges
Problem Formulation
Given the anechoic speech signal s(n) and the acoustic impulse
response h(n) we can express the reverberant speech signal as
z(n) =
n
X
j=−∞
s(j)h(n − j).
The microphone signal can be written as
x(n) = z(n) + v (n).
where v (n) denotes the additive ambient noise component.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
What is reverberation?
Motivation for Speech Dereverberation
Applications
Problem Formulation
Challenges
Problem Formulation
Ultimate Goal
Complete Dereverberation: Given the microphone signals our
objective is to estimate the anechoic speech signal s(n) up to an
arbitrary scale and time delay.
Sufficient Goal
Partial Dereverberation: Given the microphone signals our
objective is to estimate a filtered version of the anechoic speech
signal s(n).
This filter should introduce less reverberation and spectral
coloration compared to a reference acoustic channel.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
What is reverberation?
Motivation for Speech Dereverberation
Applications
Problem Formulation
Challenges
Challenges
Speech dereverberation is a blind problem.
Source Signal:
Unknown.
Non-stationary.
Acoustic Channel:
Unknown.
Time-varying.
Impulse response is very long, i.e., approx. fs · RT60 samples.
Impulse response is nonminimum-phase.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Classification
Class I: Reverberation Cancellation
Class II: Reverberation Suppression
Outline
1
Introduction
2
Existing Speech Dereverberation Techniques
3
Proposed Speech Dereverberation Technique
4
Experimental Results
5
Summary and Future Research
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Classification
Class I: Reverberation Cancellation
Class II: Reverberation Suppression
Existing Speech Dereverberation Techniques
In the context of automatic speech/speaker recognition
dereverberation can be integrated into the recognizer.
Speech dereverberation can be performed in the
Feature Domain
Cepstral Mean Normalization
Cepstral Mean and Variance Normalization
Reverberation Models [Sehr and Kellermann, 2006-2007])
Signal Domain
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Classification
Class I: Reverberation Cancellation
Class II: Reverberation Suppression
Classification
Reverberation
Suppression
Reverberation
Cancellation
Source Characteristics
Exact
Explicit
Speech Modelling
Litle
LP Residual
Enhancement
Spectral Enhancement
HERB
Temporal Envelope
Filtering
Blind Deconvolution
None
Spatial Processing
Homomorphic
Deconvolution
None
Litle
Exact
Channel Knowledge
Figure: Overview of different speech dereverberation techniques.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Classification
Class I: Reverberation Cancellation
Class II: Reverberation Suppression
Class I: Reverberation Cancellation
s(t)
Linear
System
L(t)
x(t)
Inverse
System
L−1 (t)
ŝ(t)
Unknown Environment
Two distinct approaches:
Estimate s(t) directly, or the parameters of the signal model
and the excitation signal, i.e., by treading the parameters of
the system L(t) as nuisance parameters.
Firstly, model the linear system L(t). Secondly, estimate the
parameters of the system L(t). Finally, deconvolve x(t) with
L−1 (t) to recover s(t).
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Classification
Class I: Reverberation Cancellation
Class II: Reverberation Suppression
Class I: Reverberation Cancellation
Examples (non-exhaustive list)
Blind Deconvolution (i.i.d. assumption) [Haykin, 1994].
Null-space of the spatial correlation matrix [Gannot, 2003].
Bayesian parameter estimation techniques to estimate the unknown
parameters of the speech and the channel model [Hopgood, 2000].
Problems and Limitations
Insufficiently robust to small changes in the AIR [Radlovic, 2000].
Channels cannot be identified uniquely when they contain common zeros.
Observation noise causes severe problems.
Some methods require knowledge of the order of the unknown system.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Classification
Class I: Reverberation Cancellation
Class II: Reverberation Suppression
Class II: Reverberation Suppression
Examples (non-exhaustive list)
Cepstrum Techniques (e.g., liftering in the Cepstrum domain).
Spatial Processing (e.g., delay and sum beamformer).
Linear Prediction Residual Enhancement [Gaubitch et al., 2004-2007].
Spectral Enhancement [Lebart, 2001; Habets, 2004-2007].
Problems and Limitations
a priori knowledge of the source and/or the channel is required.
Only partial dereverberation is possible.
Tendency to introduce speech distortions.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Proposed Approach
Statistical Reverberation Models
Single-Microphone Spectral Enhancement
Multi-Microphone Spectral Enhancement
Outline
1
Introduction
2
Existing Speech Dereverberation Techniques
3
Proposed Speech Dereverberation Technique
4
Experimental Results
5
Summary and Future Research
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Proposed Approach
Statistical Reverberation Models
Single-Microphone Spectral Enhancement
Multi-Microphone Spectral Enhancement
Proposed Approach
Reflections affect the desired signal in two distinct ways:
Early reflections introduce spectral coloration.
Late reflections change the waveform’s temporal envelope as
exponentially decaying tails are added at sound offsets.
Independent research has shown that the
speech fidelity and intelligibility are mainly degraded
by late reverberation.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Proposed Approach
Statistical Reverberation Models
Single-Microphone Spectral Enhancement
Multi-Microphone Spectral Enhancement
Proposed Approach
Let us split the AIR into two components:
h (n)
h(n)
he (n)
0
N
time index n
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Proposed Approach
Statistical Reverberation Models
Single-Microphone Spectral Enhancement
Multi-Microphone Spectral Enhancement
Proposed Approach
The received microphone signal x(n) can then be expressed as
n
X
x(n) =
j=n−N` +1
|
s(j)he (n − j) +
{z
ze (n)
}
n−N
X`
j=−∞
|
s(j)h` (n − j) + v (n)
{z
z` (n)
}
In the short-time Fourier transform (STFT) domain:
X (`, k) = Ze (`, k) + Z` (`, k) + V (`, k).
We aim at the suppression of late reverberation and
noise, i.e., at the estimation of the early speech
component Ze (`, k).
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Proposed Approach
Statistical Reverberation Models
Single-Microphone Spectral Enhancement
Multi-Microphone Spectral Enhancement
Polack’s Statistical Reverberation Model
Polack developed a time-domain model where an AIR is described
as a realization of a non-stationary stochastic process:
(
b(n)e−αn for n ≥ 0;
h(n) =
0
otherwise,
where b(n) is a white zero–mean Gaussian stationary noise
sequence and α is linked to the reverberation time T60 through
α,
Dr.Ir. Emanuël Habets
3 ln(10)
.
T60 fs
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Proposed Approach
Statistical Reverberation Models
Single-Microphone Spectral Enhancement
Multi-Microphone Spectral Enhancement
Generalized Statistical Reverberation Model
To model the contribution of the direct path, the AIR h(n) is
divided into two segments:

−αn 0 ≤ n < N ;

r
hd (n) = bd (n)e
−αn
h(n) = hr (n) = br (n)e
n ≥ Nr ;


0
otherwise.
The value Nr is chosen such that hd (n) contains the direct path
and that hr (n) consists of all later reflections.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Proposed Approach
Statistical Reverberation Models
Single-Microphone Spectral Enhancement
Multi-Microphone Spectral Enhancement
Late Reverberant Spectral Variance Estimation
1
The spectral variance of the reverberant signal component
zr (n) is given by
λ̂zr (`, k) = e−2α(k)R (1 − κ(k)) λ̂zr (` − 1, k)
+ κ(k) e−2α(k)R λz (` − 1, k),
where λz (`, k) = E{|Z (`, k)|2 } and κ is inversely proportional
to the Direct to Reverberation Ratio.
2
The spectral variance of the late reverberant signal
component z` (n) is given by
λz` (`, k) = e−2α(k)(N` −R) λ̂zr (` −
Dr.Ir. Emanuël Habets
N`
+ 1, k).
R
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Proposed Approach
Statistical Reverberation Models
Single-Microphone Spectral Enhancement
Multi-Microphone Spectral Enhancement
Single-Microphone Spectral Enhancement
x(n)
TF
Analysis
X(, k)
Post-Filter
Noise
Estimator
Ẑe (, k)
TF
Synthesis
ẑe (n)
λ̂v (, k)
λ̂v (, k)
Late Reverberant
Energy Estimator
λ̂z (, k)
Figure: Block diagram of the developed single-microphone speech
enhancement system.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Proposed Approach
Statistical Reverberation Models
Single-Microphone Spectral Enhancement
Multi-Microphone Spectral Enhancement
Post-Filter
Various spectral enhancement methods can be used, e.g.,
spectral subtraction and statistical methods.
We used a statistical method that is based on a Mean
Squared Error distortion measure and a Log Spectral
Amplitude fidelity criterion. The STFT coefficients of the
speech and interference are assumed to be complex Gaussian
random variables.
The resulting gain function depends on the a priori and a
posteriori Signal to Interference Ratios, and the speech
presence probability.
We developed several modifications to improve the joint
suppression of ambient noise and late reverberation.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Proposed Approach
Statistical Reverberation Models
Single-Microphone Spectral Enhancement
Multi-Microphone Spectral Enhancement
Multi-Microphone Spectral Enhancement
Until now we exploited time diversity and spectral diversity.
However, reverberation induces spatial diversity , which can be
exploited by using multiple microphones.
The late reverberant spectral variance estimate can be
improved using multiple microphones.
The speech presence probability estimation can be improved
using spatial information (Mean Squared Coherence).
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Proposed Approach
Statistical Reverberation Models
Single-Microphone Spectral Enhancement
Multi-Microphone Spectral Enhancement
Multi-Microphone Spectral Enhancement
The multi-microphone Minimum Mean Squared Error (MMSE)
estimator can be divided into a Minimum Variance Distortionless
Response (MVDR) beamformer and a single-Microphone MMSE
estimator.
X1 (, k)
..
.
XM (, k)
Multi-Channel
Y (, k)
MMSE Estimator
X1 (, k)
..
.
XM (, k)
MVDR
Y MVDR (, k)
Beamformer
Single-Channel
Y (, k)
MMSE Estimator
Figure: Multi-microphone MMSE estimator and the equivalent MVDR
beamformer and single-microphone MMSE estimator.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Single-Microphone
Multi-Microphone
Audio Demonstration
Outline
1
Introduction
2
Existing Speech Dereverberation Techniques
3
Proposed Speech Dereverberation Technique
4
Experimental Results
5
Summary and Future Research
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Single-Microphone
Multi-Microphone
Audio Demonstration
Virtual Room
Dimensions: 5 m × 4 m × 6 m
Volume: 120 m3
x1 (n)
Di
D
Reverberation time: 0.2 - 1 sec
Source-Microphone Distance: 0.25 - 4 m
xM (n)
Signal to Noise Ratio: 10 - 30 dB
Reverberation starts at N` /fs = 40 ms.
Figure: Virtual room setup.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Single-Microphone
Multi-Microphone
Audio Demonstration
Performance Evaluation Measures
Quantity: Segmental Signal to Interference Ratio (SIR):
!
P`R+N−1
2
s(n)
1 X
n=`R
SIRseg =
10 log10 P`R+N−1
|L|
(s(n) − ŝ(n))2
n=`R
[dB],
`∈L
(1)
Quality: Bark Spectral Distortion (BSD) score:
1 X
BSD =
|L|
`∈L
PKs
2
ks =1 (Ls (`, ks ) − Lŝ (`, ks ))
,
PKs
2
ks =1 (Ls (`, ks ))
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
(2)
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Single-Microphone
Multi-Microphone
Audio Demonstration
Joint Late Reverberation and Noise Suppression
5
0.4
Microphone
Processed NS
Processed RS+NS
Bark Spectral Distortion
Segmental SIR [dB]
0
0.35
−5
−10
−15
Microphone
Processed NS
Processed RS+NS
0.3
0.25
0.2
0.15
0.1
0.05
−20
0.2
0.4
0.6
0.8
Reverberation Time [s]
1
0
0.2
0.4
0.6
0.8
Reverberation Time [s]
1
Figure: Segmental SIRs and BSDs of the unprocessed microphone signal, the
processed signal after noise suppression (NS), and the processed signal after
joint reverberation and noise suppression (RS+NS). The reverberation time
varies between 0.2 and 1 s (SNR = 30 dB, D = 1 m, and N` /fs = 40 ms).
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Single-Microphone
Multi-Microphone
Audio Demonstration
Joint Late Reverberation and Noise Suppression
10
2.5
Segmental SIR [dB]
Bark Spectral Distortion
Microphone
Processed NS
Processed RS+NS
5
0
−5
−10
−15
Microphone
Processed NS
Processed RS+NS
2
1.5
1
0.5
−20
−25
0.5
1
1.5
2
2.5
3
3.5
Source−Microphone Distance [m]
4
0
0.5
1
1.5
2
2.5
3
Source−Microphone Distance [m]
3.5
4
Figure: Segmental SIRs and BSDs of the unprocessed microphone signal, the
processed signal after noise suppression (NS), and the processed signal after
joint reverberation and noise suppression (RS+NS). The source-microphone
varies between 0.25 and 4 m (SNR = 30 dB, T60 = 500 ms, and
N` /fs = 40 ms).
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Single-Microphone
Multi-Microphone
Audio Demonstration
Joint Late Reverberation and Noise Suppression
10
0
−5
−10
−15
−20
10
Microphone
Processed NS
Processed RS+NS
0.18
Bark Spectral Distortion
Segmental SIR [dB]
5
0.2
Microphone
Processed NS
Processed RS+NS
0.16
0.14
0.12
0.1
12.5
15
17.5
20
22.5
SNR [dB]
25
27.5
30
0.08
10
12.5
15
17.5 20 22.5
SNR [dB]
25
27.5
30
Figure: Segmental SIRs and BSDs of the unprocessed microphone signal, the
processed signal after noise suppression (NS), and the processed signal after
joint reverberation and noise suppression (RS+NS). The SNR of the received
signal varies between 10 and 30 dB (D = 1 m,T60 = 500 ms, and
N` /fs = 40 ms).
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Single-Microphone
Multi-Microphone
Audio Demonstration
Joint Late Reverberation and Noise Suppression
5
Bark Spectral Distortion
Segmental SRR [dB]
0
0.5
Microphone
DSB
DSB−PF
−5
−10
−15
−20
1
2
3
4
5
6
7
Number of Microphones
8
9
0.4
0.3
0.2
0.1
0
1
Microphone
DSB
DSB−PF
2
3
4
5
6
7
Number of Microphones
8
9
Figure: Segmental SIRs and BSDs of the reference microphone signal, the
DSB signal, and the DSB-PF signal. The number of microphones ranges from
1 to 9 (D = 1.5 m, T60 = 0.5 s, SNR = 30 dB, and N` /fs = 40 ms).
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Single-Microphone
Multi-Microphone
Audio Demonstration
Audio Demonstration
Frequency [kHz]
Frequency [kHz]
Microphone
4
4
0.2
0.15
2
0.1
2
4
Processed
6
8
Amplitude
0
0
Microphone
Processed
0.05
0
−0.05
−0.1
2
−0.15
0
0
2
4
Time [sec]
6
8
−0.2
0
2
4
Time [sec]
6
8
Figure: Spectrograms and waveforms of the microphone signal and processed
signal (M = 4, D = 1.5 m, T60 = 0.7 s, SNR = 20 dB, and N` /fs = 48 ms).
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Outline
1
Introduction
2
Existing Speech Dereverberation Techniques
3
Proposed Speech Dereverberation Technique
4
Experimental Results
5
Summary and Future Research
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Summary and Future Research
Summary
We developed an effective and computational efficient estimator for the
late reverberant spectral variance.
Suppression of late reverberation and ambient noise is possible using
spectral enhancement.
Future Research
Optimal fidelity criteria for speech dereverberation?
A suitable technique to equalize the spectral colouration caused by the
early reflections needs to be developed. Together with the developed
spectral enhancement technique it can provide a practical solution for
speech dereverberation.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation
Introduction
Existing Speech Dereverberation Techniques
Proposed Speech Dereverberation Technique
Experimental Results
Summary and Future Research
Thank you for your attention....
For more information visit
www.dereverberation.com and
ehabets.dereverberation.com.
Dr.Ir. Emanuël Habets
Recent Advances in Speech Dereverberation