Post on 31-Dec-2015
Blind speech dereverberation using multiple microphones
Inseon JANG, Seungjin CHOI
Intelligent Multimedia LabDepartment of Computer Science and Engineering, POSTECH
jinsn@postech.ac.krSeungjin@postech.ac.kr
2
Outline
Introduction What is the Reverberant speech ?
Previous approaches for Speech dereverberation Blind speech dereverberation using multiple microphones
Blind Equalization using multiple microphones – Single Input Multiple Output (SIMO) system
Subspace Method Deterministic Method Results
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What is the Reverberant Speech ?
Reverberant speech
cf) Noisy speech
The degrading component of the case of reverberation is dependent on previous speech data, whereas the degrading component of the case of noise speech is independent of speech.
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4
Previous approaches for Speech dereverberation
Cepstrum based approach Adaptive microphone array processing Blind Deconvolution Temporal envelope filtering Multi-Microphone sub-band envelope estimation Wavelet transform extrema clustering Maximum-kurtosis subband adaptive filtering Using LP Residual signal Using Probabilistic Models
5
Blind Equalization using multiple microphones – SIMO system (1/2)
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6
Blind Equalization using multiple microphones – SIMO system (2/2)
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Subspace Method
By orthogonality between the noise and the signal subspace,the column of are orthogonal to any vector in the noise subspace
for
Subspace-Based Parameter Estimation SchemeMinimization of the quadratic form
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8
Deterministic Method (1/2)
Cross Relation Approach
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Deterministic Method (2/2)
Channel estimate
Equivalently, the channel estimate can be obtained from the singular vector of associated with the smallest singular value
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Result (1/3)Reverberant signal and Dereverberant signal
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Result (2/3)Dereverberation using Subspace method
Channel length : 654 Test size : 5000
Result MSE : 1.3608e-007
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Result (3/3)Dereverberation using Deterministic method
Channel length : 654 Test size : 1000
Result MSE : 7.7074e-018