In this paper we compare the performance of different algorithms employed in solving frequency domain blind source separation of convolutive mixtures. The convolutive model is an extension of the instantaneous one and it allows to relax the hypothesis of a linear mixing process in which all the sources are supposed to reach the electrodes at the same time. This test is carried out in the freque...

This paper addresses the problem of demixing professionally produced music, i.e., recovering the musical source signals that compose a (2-channel stereo) commercial mix signal. Inspired by previous studies using MIDI synthesized or hummed signals as external references, we propose to use the multitrack signals of a cover interpretation to guide the separation process with a relevant initializat...

In this paper we present a novel method for Blind Speech Separation of convolutive speech signals of moving speakers in highly reverberant rooms. The separation network used is a hybrid neural network, which performs separation of convolutive speech mixtures in the time domain, without any prior knowledge of the propagation media, based on the Maximum Likelihood Estimation (MLE) principle. The ...

This paper describes Independent Component Analysis (ICA) based fixed-point algorithm for the blind separation of the convolutive mixture of speech, picked-up by a linear microphone array. The proposed algorithm extracts independent sources by nonGaussianizing the Time-Frequency Series of Speech (TFSS) in a deflationary way. The degree of non-Gaussianization is measured by negentropy. The relat...

In cocktail party listening scenarios, the human brain is able to separate competing speech signals. However, the signal processing implemented by the brain to perform cocktail party listening is not well understood. Here, we trained two separate convolutive autoencoder deep neural networks (DNN) to separate monaural and binaural mixtures of two concurrent speech streams. We then used these DNN...

An on-line adaptive blind source separation algorithm for the separation of convolutive mixtures of cyclostationary source signals is proposed. The algorithm is derived by a p plying natural gradient iterative learning to the novel cost function which is delined according to the wide sense cyclostationarity of signals. The efficiency of the algorithm is supported by simulations, which show that...

A joint diagonalization algorithm for convolutive blind source separation by explicitly exploiting the nonstationarity and second order statistics of signals is proposed. The algorithm incorporates a non-unitary penalty term within the cross-power spectrum based cost function in the frequency domain. This leads to a modification of the search direction of the gradient-based descent algorithm an...

Blind separation of independent sources from their convolutive mixtures is a problem in many real world multi-sensor applications. In this paper we present a solution to this problem based on the information maximization principle, which was recently proposed by Bell and Sejnowski for the case of blind separation of instantaneous mixtures. We present a feedback network architecture capable of c...

Blind separation of independent sources from their convolutive mixtures is a problem in many real world multi-sensor applications. In this paper we present a solution to this problem based on the information maximization principle, which was recently proposed by Bell and Sejnowski for the case of blind separation of instantaneous mixtures. We present a feedback network architecture capable of c...

Convolutive non-negative matrix factorization (CNMF) and its sparse version, convolutive non-negative sparse coding (CNSC), exhibit great success in speech processing. A particular limitation of the current CNMF/CNSC approaches is that the convolution ranges of the bases in learning are identical, resulting in patterns covering the same time span. This is obvious unideal as most of sequential s...