I-Divergence-based dereverberation method with auxiliary function approach

This paper presents a dereverberation method based on I-divergence minimization, which is particularly suitable for music signals. Existing dereverberation methods, including one designed for music, sometimes distort instrument sounds and make staccato-like tones. The problems with the Itakura-Saito-divergence-based formulation of the existing methods are 1) their tendency to excessive suppression of direct sound and 2) the difficulty of incorporating and optimizing sophisticated source models suitable for music signals. The proposed I-divergence-based method can mitigate these problems. Employing the I-divergence measure enables us to avoid the direct sound suppression problem and to use powerful music spectrum models without complicating its optimization. We develop a convergence-guaranteed parameter estimation algorithm based on the auxiliary function approach. Experimental results reveal the effectiveness of the proposed dereverberation method.

[1]  P. Smaragdis,et al.  Non-negative matrix factorization for polyphonic music transcription , 2003, 2003 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (IEEE Cat. No.03TH8684).

[2]  Malcolm Slaney,et al.  Acoustic Chord Transcription and Key Extraction From Audio Using Key-Dependent HMMs Trained on Synthesized Audio , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[3]  Masataka Goto.  Active Music Listening Interfaces Based on Signal Processing , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[4]  Tomohiro Nakatani,et al.  Fast algorithm for conditional separation and dereverberation , 2009, 2009 17th European Signal Processing Conference.

[5]  Tomohiro Nakatani,et al.  Music dereverberation using harmonic structure source model and Wiener filter , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  Masataka Goto,et al.  RWC Music Database: Popular, Classical and Jazz Music Databases , 2002, ISMIR.

[7]  James Anderson Moorer,et al.  On the segmentation and analysis of continuous musical sound by digital computer , 1975 .

[8]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[9]  Hirokazu Kameoka,et al.  A Multipitch Analyzer Based on Harmonic Temporal Structured Clustering , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  Tomohiro Nakatani,et al.  An integrated method for blind separation and dereverberation of convolutive audio mixtures , 2008, 2008 16th European Signal Processing Conference.

[11]  Hirokazu Kameoka,et al.  Extraction of Multiple Fundamental Frequencies from Polyphonic Music Using Harmonic Clustering , 2003 .

[12]  Anssi Klapuri,et al.  Multiple fundamental frequency estimation based on harmonicity and spectral smoothness , 2003, IEEE Trans. Speech Audio Process..

[13]  H. Kameoka,et al.  Convergence-guaranteed multiplicative algorithms for nonnegative matrix factorization with β-divergence , 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.