Performance analysis of the covariance subtraction method for relative transfer function estimation and comparison to the covariance whitening method

Microphone array processing utilize spatial separation between the desired speaker and interference signal for speech enhancement. The transfer functions (TFs) relating the speaker component at a reference microphone with all other microphones, denoted as the relative TFs (RTFs), play an important role in beamforming design criteria such as minimum variance distortionless response (MVDR) and speech distortion weighted multichannel Wiener filter (SDW-MWF). Two common methods for estimating the RTF are surveyed here, namely, the covariance subtraction (CS) and the covariance whitening (CW) methods. We analyze the performance of the CS method theoretically and empirically validate the results of the analysis through extensive simulations. Furthermore, empirically comparing the methods performances in various scenarios evidently shows thats the CW method outperforms the CS method.

[1]  Israel Cohen,et al.  Relative transfer function identification using speech signals , 2004, IEEE Transactions on Speech and Audio Processing.

[2]  Sharon Gannot,et al.  Geometrically Constrained TRINICON-based relative transfer function estimation in underdetermined scenarios , 2013, 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.

[3]  Ehud Weinstein,et al.  Signal enhancement using beamforming and nonstationarity with applications to speech , 2001, IEEE Trans. Signal Process..

[4]  L. J. Griffiths,et al.  An alternative approach to linearly constrained adaptive beamforming , 1982 .

[5]  O. L. Frost,et al.  An algorithm for linearly constrained adaptive array processing , 1972 .

[6]  Loukianos Spyrou,et al.  Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on , 2017, ICASSP 2017.

[7]  Marc Moonen,et al.  Low-rank Approximation Based Multichannel Wiener Filter Algorithms for Noise Reduction with Application in Cochlear Implants , 2014, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[8]  N. R. Goodman Statistical analysis based on a certain multivariate complex Gaussian distribution , 1963 .

[9]  Marc Moonen,et al.  Speech Distortion Weighted Multichannel Wiener Filtering Techniques for Noise Reduction , 2005 .

[10]  Sharon Gannot,et al.  Adaptive Beamforming and Postfiltering , 2008 .

[11]  Jacob Benesty,et al.  The MVDR Beamformer for Speech Enhancement , 2010 .

[12]  B.D. Van Veen,et al.  Beamforming: a versatile approach to spatial filtering , 1988, IEEE ASSP Magazine.

[13]  Israel Cohen,et al.  Performance of the SDW-MWF With Randomly Located Microphones in a Reverberant Enclosure , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[14]  Israel Cohen,et al.  Multichannel Eigenspace Beamforming in a Reverberant Noisy Environment With Multiple Interfering Speech Signals , 2009, IEEE Transactions on Audio, Speech, and Language Processing.

[15]  Sharon Gannot,et al.  Time difference of arrival estimation of speech source in a noisy and reverberant environment , 2005, Signal Process..

[16]  Sharon Gannot,et al.  Binaural Linearly Constrained Minimum Variance Beamformer for Hearing Aid Applications , 2012, IWAENC.

[17]  Zbynek Koldovský,et al.  Sparse target cancellation filters with application to semi-blind noise extraction , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[18]  Volker Hohmann,et al.  Binaural cue preservation for hearing aids using multi-channel wiener filter with instantaneous ITF preservation , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[19]  Jacob Benesty,et al.  A Study of the LCMV and MVDR Noise Reduction Filters , 2010, IEEE Transactions on Signal Processing.

[20]  P. Stoica,et al.  Robust Adaptive Beamforming , 2013 .

[21]  Marc Moonen,et al.  Distributed Node-Specific LCMV Beamforming in Wireless Sensor Networks , 2012, IEEE Transactions on Signal Processing.