Estimating direct-to-reverberant energy ratio based on spatial correlation model segregating direct sound and reverberation

A new approach for estimating the direct-to-reverberant energy ratio (DRR) using a microphone array is proposed. The method is based on amodel of a spatial correlation matrix that segregates direct sound and reverberation. It estimates DRR from the power spectra of both components, which are derived from the correlation matrix of the observed signal. In experiments performed in simulated and actual reverberant environments, the proposed method mostly succeeded in estimating DRR accurately. We also present speech enhancement using binary masking as an example of an application of the estimated DRR. By utilization of the DRR as a factor to discriminate the distances of speakers, separation of speech signals whose sources were located in the same direction but at different distances was achieved.

[1]  Kazuho Ono,et al.  Separation of Sound Sources Propagated in the Same Direction , 2005, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[2]  B.C. Wheeler,et al.  Acoustic scene analysis using estimated impulse responses , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[3]  Martin Cooke,et al.  BINAURAL DISTANCE PERCEPTION BASED ON DIRECT-TO-REVERBERANT ENERGY RATIO , 2008 .

[4]  A. Bronkhorst,et al.  Auditory distance perception in humans : A summary of past and present research , 2005 .

[5]  Martin Cooke,et al.  Binaural Estimation of Sound Source Distance via the Direct-to-Reverberant Energy Ratio for Static and Moving Sources , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[6]  Hideo Suzuki,et al.  The nature and technology of acoustic space , 1995 .

[7]  Marc Moonen,et al.  Design of far-field and near-field broadband beamformers using eigenfilters , 2003, Signal Process..

[8]  Arun Ross,et al.  Microphone Arrays , 2009, Encyclopedia of Biometrics.

[9]  Yusuke Hioka,et al.  Enhancement of Sound Sources Located within a Particular Area Using a Pair of Small Microphone Arrays , 2008, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[10]  Yutaka Kaneda,et al.  Sound source segregation based on estimating incident angle of each frequency component of input signals acquired by multiple microphones , 2001 .

[11]  Hiroshi Sawada,et al.  Underdetermined sparse source separation of convolutive mixtures with observation vector clustering , 2006, 2006 IEEE International Symposium on Circuits and Systems.

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