Differential fast fixed-point algorithms for underdetermined instantaneous and convolutive partial blind source separation

This paper concerns underdetermined linear instantaneous and convolutive blind source separation (BSS), i.e., the case when the number of observed mixed signals is lower than the number of sources.We propose partial BSS methods, which separate supposedly nonstationary sources of interest (while keeping residual components for the other, supposedly stationary, "noise" sources). These methods are based on the general differential BSS concept that we introduced before. In the instantaneous case, the approach proposed in this paper consists of a differential extension of the FastICA method (which does not apply to underdetermined mixtures). In the convolutive case, we extend our recent time-domain fast fixed-point C-FICA algorithm to underdetermined mixtures. Both proposed approaches thus keep the attractive features of the FastICA and C-FICA methods. Our approaches are based on differential sphering processes, followed by the optimization of the differential nonnormalized kurtosis that we introduce in this paper. Experimental tests show that these differential algorithms are much more robust to noise sources than the standard FastICA and C-FICA algorithms.Comment: this paper describes our differential FastICA-like algorithms for linear instantaneous and convolutive underdetermined mixture

Power-Weighted Divergences for Relative Attenuation and Delay Estimation

Power-weighted estimators have recently been proposed for relative attenuation and delay estimation in blind source separation. Their provenance lies in the observation that speech is approximately windowed-disjoint orthogonal (WDO) in the time-frequency (TF) domain; it has been reported that using WDO, derived from TF representations of speech, improves mixing parameter estimation. We show that power-weighted relative attenuation and delay estimators can be derived from a particular case of a weighted Bregman divergence. We then propose a wider class of estimators, which we tune to give better parameter estimates for speech

Model-Based Separation in Humans and Machines

Comparing human performance on source separation with different automatic approaches, and arguing for (a) using models, and (b) concentrating on the content, not the signal per se

Speech Separation in Humans and Machines

An overview of the problem of separating speech in acoustic mixtures, including some perceptual results, brief introductions to ICA and CASA, and a pitch for model-based analysis

Studies on Real-Time Oriented Sound Source DOA Estimation Based on Sparseness

九州工業大学博士学位論文 学位記番号:情工博甲第295号 学位授与年月日:平成27年3月25日第1章 序論|第2章 DOA 推定の概要|第3章 音声のDOA 推定に関する予備的検討|第4章 フレーム単位のDOA 推定|第5章 シミュレーションおよび考察|第6章 結論九州工業大学平成26年

Auditory Scene Analysis in Humans and Machines

Tutorial on auditory scene analysis and source separation in humans and machines

Underdetermined Blind Source Separation in Echoic Environments Using DESPRIT

The DUET blind source separation algorithm can demix an arbitrary number of speech signals using M=2 anechoic mixtures of the signals. DUET however is limited in that it relies upon source signals which are mixed in an anechoic environment and which are sufficiently sparse such that it is assumed that only one source is active at a given time frequency point. The DUET-ESPRIT (DESPRIT) blind source separation algorithm extends DUET to situations where M≥2 sparsely echoic mixtures of an arbitrary number of sources overlap in time frequency. This paper outlines the development of the DESPRIT method and demonstrates its properties through various experiments conducted on synthetic and real world mixtures