Blind reverberation energy estimation using exponential averaging with attack and release time constants for hearing aids

Abstract Dereverberation signal processing is necessary for hearing-aid systems because reverberation degrades speech intelligibility in reverberant environments. In general, hearing-aid systems require low computational cost and real-time signal processing. The spectral subtraction (SS) method is a simple and frequently used technique which is used not only for noise-reduction but for dereverberation as well. To perform dereverberation methods based on SS for hearing aids, the reverberation energy must be estimated or measured. In this paper, the SS-based blind estimation of reverberation energy in a single-channel speech signal is proposed by using exponential averaging with attack and release time constants. The estimation error, which is the difference between the true and estimated reverberation energy, was used for evaluation. The estimation error of the proposed method was compared with the results of the method proposed by Lebart et al., which is a well-known non-blind SS-based dereverberation method. According to the results, the reverberation energy was more accurately estimated when the reverberation time was longer than 0.6 s, and the estimation error of the proposed method was approximately half of that of the well-known non-blind method by Lebart.

[1]  Longbiao Wang,et al.  Dereverberation and denoising based on generalized spectral subtraction by multi-channel LMS algorithm using a small-scale microphone array , 2012, EURASIP Journal on Advances in Signal Processing.

[2]  Michael S. Brandstein,et al.  Microphone Arrays - Signal Processing Techniques and Applications , 2001, Microphone Arrays.

[3]  Eap Emanuël Habets Single- and multi-microphone speech dereverberation using spectral enhancement , 2007 .

[4]  Bayya Yegnanarayana,et al.  Enhancement of reverberant speech using LP residual signal , 2000, IEEE Trans. Speech Audio Process..

[5]  Unto K. Laine,et al.  Frequency-warped signal processing for audio applications , 2000 .

[6]  Tomohiro Nakatani,et al.  Efficient blind dereverberation framework for automatic speech recognition , 2005, INTERSPEECH.

[7]  F. Massey The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[8]  E. Zwicker,et al.  Subdivision of the audible frequency range into critical bands , 1961 .

[9]  J. Pickett,et al.  Monaural and binaural speech perception through hearing aids under noise and reverberation with normal and hearing-impaired listeners. , 1974, Journal of speech and hearing research.

[10]  Patrick A. Naylor,et al.  Speech Dereverberation , 2010 .

[11]  Ken'ichi Furuya,et al.  Robust Speech Dereverberation Using Multichannel Blind Deconvolution With Spectral Subtraction , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[12]  James M. Kates,et al.  Multichannel Dynamic-Range Compression Using Digital Frequency Warping , 2005, EURASIP J. Adv. Signal Process..

[13]  J.-M. Boucher,et al.  A New Method Based on Spectral Subtraction for Speech Dereverberation , 2001 .

[14]  Jont B. Allen,et al.  Invertibility of a room impulse response , 1979 .

[15]  S. W. Roberts,et al.  Control Chart Tests Based on Geometric Moving Averages , 2000, Technometrics.

[16]  J. S. Bradley,et al.  Reverberation time and maximum background-noise level for classrooms from a comparative study of speech intelligibility metrics. , 2000, Journal of the Acoustical Society of America.

[17]  Marc Moonen,et al.  Subspace Methods for Multimicrophone Speech Dereverberation , 2003, EURASIP J. Adv. Signal Process..

[18]  J. Oghalai,et al.  Chapter 37 – Cochlear Hearing Loss , 2005 .

[19]  Michael S. Brandstein,et al.  A microphone array system for speech source localization, denoising, and dereverberation , 2002 .

[20]  Thomas Esch,et al.  Model-Based Dereverberation Preserving Binaural Cues , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

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

[22]  Tomohiro Nakatani,et al.  Harmonicity-Based Blind Dereverberation for Single-Channel Speech Signals , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[23]  Peter Vary,et al.  Low Delay Noise Reduction and Dereverberation for Hearing Aids , 2009, EURASIP J. Adv. Signal Process..

[24]  Reinhold Häb-Umbach,et al.  Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[25]  Rui Wang,et al.  Speech dereverberation method based on spectral subtraction and spectral line enhancement , 2016 .

[26]  J. Polack La transmission de l'energie sonore dans les salles , 1988 .

[27]  Masashi Unoki,et al.  A speech dereverberation method based on the MTF concept using adaptive time-frequency divisions , 2003, 2004 12th European Signal Processing Conference.