source number estimation and clustering for undetermined blind source separation
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Source Number Estimation and Clustering for Undetermined Blind Source Separation. Benedikt Loesch and Bin Yang University of Stuttgart Chair of System Theory and Signal Processing International Workshop on Acoustic Echo and Noise Control, 2008. Presenter Chia-Cheng Chen. Outline. - PowerPoint PPT PresentationTRANSCRIPT
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Source Number Estimation and Clustering for Undetermined BlindSource Separation
Benedikt Loesch and Bin Yang
University of StuttgartChair of System Theory and Signal Processing
International Workshop on Acoustic Echo and Noise Control, 2008
Presenter Chia-Cheng Chen 1
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Introduction
Observation Vector Clustering
Source Number Estimation
Experimental results
Conclusion
Outline
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The task of blind source separation is to separate M
(possibly) convolutive mixtures xm[i],m = 1, . . . ,M
into N different source signals.
Present an algorithm call NOSET (Number of Source
Estimation Technique)
Introduction
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Short Time Fourier transform (STFT)
Three steps
◦Normalization
◦Clustering
◦Reconstruction
Observation Vector Clustering(1/3)
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Normalization
◦ The normalization is performed with respect to a reference
sensor J [4]
◦ Unit-norm normalization
Observation Vector Clustering(2/3)
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Clustering◦K-means
Reconstruction [4]
Observation Vector Clustering(3/3)
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The phase difference among different sensors is large enough. In the low-frequency region, this is not the case and the phase estimate is rather noisy.
Only one source is dominant at a TF point [k, l].
Source Number Estimation(1/6)
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Selection of One-Source TF Points
Power of source n
Selection of reliable TF points
Source Number Estimation(2/6)
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DOA Estimation◦ time delay δm for sensor m
Source Number Estimation(3/6)
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Source Number Estimation(4/6)
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Source Number Estimation(5/6)
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Source Number Estimation(6/6)
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Frequency of fs = 8 kHz and a cross-array with M = 5 microphones
16 sets of 6 speech signals (3 male, 3 female, different for each of the 16 sets)
SNR was between 20 and 30 dB
Typical values are: fl = 250Hz, t2 = 20 dB, t3 = 0.2
Experimental results(1/3)
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Experimental results(2/3)
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Experimental results(3/3)
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Presented the NOSET algorithm to estimate the number of sources in blind source separation.
It relies on DOA estimation at selected one-source TF points and works in both overdetermined and underdetermined situations.
Conclusion
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