Chapter 19 – Audio applications

Publisher Summary This chapter discusses the application of the blind source separation (BSS) techniques to the separation of audio signals, with main emphasis on convolutive independent component analysis (ICA) and sparse component analysis (SCA). The need for BSS arises with various real-world signals, including meeting recordings, hearing aid signals, music CDs, and radio broadcasts. These signals are obtained via different techniques, which result in different signal properties. In all source mixing situations, the objective of BSS is to extract one or several source signals from the observed multichannel mixture signal, with other source signals being regarded as undesired noise. The signals of interest depend on the application, for instance, in the context of speech enhancement for mobile phones, the only source signal of interest is the user's speech. Undesired sources may then include speech signals from surrounding people and environmental noises produced by cars, wind, or rain. On the contrary, the so-called cocktail-party application refers to the situation when the observed mixture signal results from several people simultaneously speaking in a room and all speech signals are of interest. Noise may then originate from clinking glasses or footsteps.

[1]  Hiroshi Sawada,et al.  Grouping Separated Frequency Components by Estimating Propagation Model Parameters in Frequency-Domain Blind Source Separation , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[2]  Hiroshi Sawada,et al.  A robust and precise method for solving the permutation problem of frequency-domain blind source separation , 2004, IEEE Transactions on Speech and Audio Processing.

[3]  Christian Jutten,et al.  Blind source separation for convolutive mixtures , 1995, Signal Process..

[4]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Yannick Deville,et al.  Temporal and time-frequency correlation-based blind source separation methods. Part I: Determined and underdetermined linear instantaneous mixtures , 2007, Signal Process..

[6]  Lucas C. Parra,et al.  Convolutive blind separation of non-stationary sources , 2000, IEEE Trans. Speech Audio Process..

[7]  Rémi Gribonval,et al.  Performance measurement in blind audio source separation , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[8]  Yannick Deville,et al.  A time-frequency blind signal separation method applicable to underdetermined mixtures of dependent sources , 2005, Signal Process..

[9]  Emmanuel Vincent,et al.  Musical source separation using time-frequency source priors , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  Yannick Deville,et al.  Time-domain fast fixed-point algorithms for convolutive ICA , 2006, IEEE Signal Processing Letters.

[11]  Paris Smaragdis,et al.  Blind separation of convolved mixtures in the frequency domain , 1998, Neurocomputing.

[12]  Volker Hohmann,et al.  Combined Estimation of Spectral Envelopes and Sound Source Direction of Concurrent Voices by Multidimensional Statistical Filtering , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[13]  Hiroshi Sawada,et al.  Evaluation of separation and dereverberation performance in frequency domain blind source separation , 2004 .

[14]  Shoko Araki,et al.  Equivalence between Frequency-Domain Blind Source Separation and Frequency-Domain Adaptive Beamforming for Convolutive Mixtures , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[15]  Scott Rickard,et al.  Blind separation of speech mixtures via time-frequency masking , 2004, IEEE Transactions on Signal Processing.

[16]  Christopher V. Alvino,et al.  Geometric source separation: merging convolutive source separation with geometric beamforming , 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).

[17]  Scott C. Douglas,et al.  Natural gradient multichannel blind deconvolution and speech separation using causal FIR filters , 2005, IEEE Trans. Speech Audio Process..

[18]  Yannick Deville,et al.  Self-adaptive separation of convolutively mixed signals with a recursive structure. Part II: Theoretical extensions and application to synthetic and real signals , 1999, Signal Process..

[19]  Douglas L. Jones,et al.  Performance of time- and frequency-domain binaural beamformers based on recorded signals from real rooms. , 2004, The Journal of the Acoustical Society of America.

[20]  Rémi Gribonval,et al.  Oracle estimators for the benchmarking of source separation algorithms , 2007, Signal Process..

[21]  Diego H. Milone,et al.  Objective quality evaluation in blind source separation for speech recognition in a real room , 2007, Signal Process..

[22]  James P. Reilly,et al.  A frequency domain method for blind source separation of convolutive audio mixtures , 2005, IEEE Transactions on Speech and Audio Processing.

[23]  Frank Ehlers,et al.  Blind separation of convolutive mixtures and an application in automatic speech recognition in a noisy environment , 1997, IEEE Trans. Signal Process..

[24]  Gregg E Trahey,et al.  Adaptive Clutter Filtering via Blind Source Separation for Two-Dimensional Ultrasonic Blood Velocity Measurement , 2002, Ultrasonic imaging.

[25]  Nikolaos Mitianoudis,et al.  Audio source separation of convolutive mixtures , 2003, IEEE Trans. Speech Audio Process..

[26]  Y. Deville,et al.  Time–frequency ratio-based blind separation methods for attenuated and time-delayed sources , 2005 .

[27]  DeLiang Wang,et al.  Binaural segregation in multisource reverberant environments. , 2006, The Journal of the Acoustical Society of America.

[28]  Ian S. Burnett,et al.  An analysis of the limitations of blind signal separation application with speech , 2006, Signal Process..