Joint blind dereverberation and separation of speech mixtures

This paper proposes a method for jointly performing blind source separation (BSS) and blind dereverberation (BD) for speech mixtures. In most of the previous studies, BSS and BD have been explored separately. It is common that the performance of the speech separation algorithms deteriorates with the increase of room reverberations. Also most of the dereverberation algorithms rely on the availability of room impulse responses (RIRs) which are not readily accessible in practice. Therefore in this work the dereverberation and separation method are combined to mitigate the effects of room reverberations on the speech mixtures and hence to improve the separation performance. As required by the dereverberation algorithm, a step for blind estimation of reverberation time (RT) is used to estimate the decay rate of reverberations directly from the reverberant speech signal (i.e., speech mixtures) by modeling the decay as a Laplacian random process modulated by a deterministic envelope. Hence the developed algorithm works in a blind manner, i.e., directly dealing with the reverberant speech signals without explicit information from the RIRs. Evaluation results in terms of signal to distortion ratio (SDR) and segmental signal to reverberation ratio (SegSRR) reveal that using this method the performance of the separation algorithm that we have developed previously can be further enhanced. © 2012 EURASIP

An analysis of the limitations of blind signal separation application with speech

Blind Signal Separation (BSS) techniques are commonly employed in the separation of speech signals, using Independent Component Analysis (ICA) as the criterion for separation. This paper investigates the viability of employing ICA for real-time speech separation (where short frame sizes are the norm). The relationship between the statistics of speech and the assumption of statistical independence (at the core of ICA) is examined over a range of frame sizes. The investigation confirms that statistical independence is not a valid assumption for speech when divided into the short frames appropriate to real-time separation. This is primarily due to the quasi-stationary nature of speech over the temporal short term. We conclude that employing ICA for real-time speech separation will always result in limited performance due to a fundamental failure to meet the strict assumptions of ICA

Semi-blind speech-music separation using sparsity and continuity priors

In this paper we propose an approach for the problem of single channel source separation of speech and music signals. Our approach is based on representing each source's power spectral density using dictionaries and nonlinearly projecting the mixture signal spectrum onto the combined span of the dictionary entries. We encourage sparsity and continuity of the dictionary coefficients using penalty terms (or log-priors) in an optimization framework. We propose to use a novel coordinate descent technique for optimization, which nicely handles nonnegativity constraints and nonquadratic penalty terms. We use an adaptive Wiener filter, and spectral subtraction to reconstruct both of the sources from the mixture data after corresponding power spectral densities (PSDs) are estimated for each source. Using conventional metrics, we measure the performance of the system on simulated mixtures of single person speech and piano music sources. The results indicate that the proposed method is a promising technique for low speech-to-music ratio conditions and that sparsity and continuity priors help improve the performance of the proposed system

Two-channel speech denoising through minimum tracking

A blind two-channel interference reduction algorithm to suppress localised interferers in reverberant environments is presented. The algorithm requires neither knowledge of source positions nor a speech-free noise reference. The goal is to estimate the speech signal as observed at one of the microphones, without any additional filtering effects that are typical in convolutive blind source separation

Fast and accurate methods of independent component analysis: A survey

summary:This paper presents a survey of recent successful algorithms for blind separation of determined instantaneous linear mixtures of independent sources such as natural speech or biomedical signals. These algorithms rely either on non-Gaussianity, nonstationarity, spectral diversity, or on a combination of them. Performance of the algorithms will be demonstrated on separation of a linear instantaneous mixture of audio signals (music, speech) and on artifact removal in electroencephalogram (EEG)