Utterance-based speech dereverberation using blind channel estimation and multichannel equalization

Speech dereverberation considering noisy environment as well as speaker's movement is a challenging task. In this paper, we present an utterance-based noise-robust speech dereverberation technique that is suitable for non-stationary speaker. The acoustic impulse responses (AIRs) between the speaker and microphone array are estimated using the spectrally constrained frequency-domain least-mean-squares (LMS) algorithm. The AIRs are then equalized using the iterative multiple-input/output inverse theorem (MINT). It is assumed that the speaker stays still within an utterance, however, the speaker changes his/her position between the utterances. The simulation experiments conducted in various reverberant environment and speaker's position demonstrate that the proposed method can satisfactorily improve the perceptual quality of the noisy reverberated speech.

[1]  Md. Kamrul Hasan,et al.  Robust multichannel least mean square-type algorithms with fast decaying transient for blind identification of acoustic channels , 2008 .

[2]  Les E. Atlas,et al.  Acoustic diversity for improved speech recognition in reverberant environments , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3]  Marc Delcroix,et al.  Inverse Filtering for Speech Dereverberation Less Sensitive to Noise and Room Transfer Function Fluctuations , 2007, EURASIP J. Adv. Signal Process..

[4]  Md. Kamrul Hasan,et al.  Noise Robust Multichannel Frequency-Domain LMS Algorithms for Blind Channel Identification , 2008, IEEE Signal Processing Letters.

[5]  S. Boll,et al.  Suppression of acoustic noise in speech using spectral subtraction , 1979 .

[6]  Emanuel A. P. Habets,et al.  A System-Identification-Error-Robust Method for equalization of multichannel acoustic systems , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  Jont B. Allen,et al.  Image method for efficiently simulating small‐room acoustics , 1976 .

[8]  Yi Hu,et al.  Evaluation of Objective Quality Measures for Speech Enhancement , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[9]  Jacob Benesty,et al.  A class of frequency-domain adaptive approaches to blind multichannel identification , 2003, IEEE Trans. Signal Process..

[10]  Masato Miyoshi,et al.  Inverse filtering of room acoustics , 1988, IEEE Trans. Acoust. Speech Signal Process..

[11]  Toufiqul Islam,et al.  Robust Speech Dereverberation Based on Blind Adaptive Estimation of Acoustic Channels , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[12]  M. Sondhi,et al.  On the evaluation of estimated impulse responses , 1998, IEEE Signal Processing Letters.

[13]  Biing-Hwang Juang,et al.  Robust blind dereverberation of speech signals based on characteristics of short-time speech segments , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[14]  Stefan Goetze,et al.  Regularization for Partial Multichannel Equalization for Speech Dereverberation , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[15]  Masahito Togami,et al.  Optimized Speech Dereverberation From Probabilistic Perspective for Time Varying Acoustic Transfer Function , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[16]  Lang Tong,et al.  A new approach to blind identification and equalization of multipath channels , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[17]  Parham Aarabi,et al.  Low-Power Dual-Microphone Speech Enhancement Using Field Programmable Gate Arrays , 2007, IEEE Transactions on Signal Processing.

[18]  James R. Hopgood,et al.  Block-Based TVAR Models for Single-Channel Blind Dereverberation of Speech from a Moving Speaker , 2007, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing.