Performance measurement in blind audio source separation

In this paper, we discuss the evaluation of blind audio source separation (BASS) algorithms. Depending on the exact application, different distortions can be allowed between an estimated source and the wanted true source. We consider four different sets of such allowed distortions, from time-invariant gains to time-varying filters. In each case, we decompose the estimated source into a true source part plus error terms corresponding to interferences, additive noise, and algorithmic artifacts. Then, we derive a global performance measure using an energy ratio, plus a separate performance measure for each error term. These measures are computed and discussed on the results of several BASS problems with various difficulty levels

[1]  William C. Treurniet,et al.  Perceptual Quality Assessment for Digital Audio: PEAQ-The New ITU Standard for Objective Measurement of the Perceived Audio Quality , 1999 .

[2]  Kiyohiro Shikano,et al.  High-fidelity blind separation for convolutive mixture of acoustic signals using SIMO-model-based independent component analysis , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

[3]  Barak A. Pearlmutter,et al.  Blind Source Separation by Sparse Decomposition in a Signal Dictionary , 2001, Neural Computation.

[4]  Andreas Ziehe,et al.  An approach to blind source separation based on temporal structure of speech signals , 2001, Neurocomputing.

[5]  John H. L. Hansen,et al.  Discrete-Time Processing of Speech Signals , 1993 .

[6]  Rémi Gribonval Sparse decomposition of stereo signals with Matching Pursuit and application to blind separation of more than two sources from a stereo mixture , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  Daniel Patrick Whittlesey Ellis,et al.  Prediction-driven computational auditory scene analysis , 1996 .

[8]  Olivier Cappé Techniques de reduction de bruit pour la restauration d'enregistrements musicaux , 1993 .

[9]  Kiyohiro Shikano,et al.  SIMO-Model-Based Independent Component Analysis for High-Fidelity Blind Separation of Acoustic Signals , 2003 .

[10]  Cédric Févotte,et al.  Two contributions to blind source separation using time-frequency distributions , 2004, IEEE Signal Processing Letters.

[11]  M. Hulle Clustering approach to square and non-square blind source separation , 1999 .

[12]  Özgür Yilmaz,et al.  Blind separation of disjoint orthogonal signals: demixing N sources from 2 mixtures , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[13]  R. Gribonval,et al.  Proposals for Performance Measurement in Source Separation , 2003 .

[14]  Igor Stravinsky,et al.  A TENTATIVE TYPOLOGY OF AUDIO SOURCE SEPARATION TASKS , 2003 .

[15]  Jean-Francois Cardoso,et al.  Blind signal separation: statistical principles , 1998, Proc. IEEE.

[16]  Paris Smaragdis,et al.  Evaluation of blind signal separation methods , 1999 .