Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF), a method for finding parts-based representations of non-negative data. Here, we present an extension to convolutive NMF that includes a sparseness constraint, ...

In this paper it is shown experimentally that a new blind signal separation method in the frequency domain improves significantly the speaker signal to interference ratio (SIR) and the phoneme recognition score of a continuous speech, speaker-independent acoustic decoder in a two-simultaneousspeaker environment. The implemented two-sensor separation method is based on evolutionary minimization ...

The number of source signals in a noisy convolutive mixture is determined based on the exact log-likelihoods of the candidate models. In (Olsson and Hansen, 2004), a novel probabilistic blind source separator was introduced that is based solely on the time-varying second-order statistics of the sources. The algorithm, known as ‘KaBSS’, employs a Gaussian linear model for the mixture, i.e. AR mo...

This paper addresses the problem of multichannel audio source separation in under-determined reverberant mixtures. We target a semi-blind scenario assuming that the mixing lters are known. The proposed method consists in working directly with the time-domain mixture signals. This approach makes it possible to accurately represent the convolutive mixing process, it is therefore suitable for the ...

If two wide sense stationary time series are correlated then one can be used to predict the other. The reduced rank Wiener filter is the rank constrained linear operator which maps the current value of one time series to an estimate of the current value of the other time series in an optimal way. A closed form solution exists for the reduced rank Wiener filter. This paper studies the problem of...

Decomposition of mixed information into its constituent components has been very useful in many applications such as acoustics, communications, and biomedicine. Eigenvalue decomposition (EVD), singular value decomposition (SVD), and independent component analysis (ICA) based on various criteria such as uncorrelatedness, independency, minimizing mutual information, and differences in distributio...

This paper derives two spatio–temporal extensions of the well-known FastICA algorithm of Hyvärinen and Oja that are applicable to the convolutive blind source separation task. Our time–domain algorithms combine multichannel spatio–temporal prewhitening via multistage least-squares linear prediction with novel adaptive procedures that impose paraunitary constraints on the multichannel separation...

We propose a method of ICA for separating convolutive mixtures of acoustic signals. The acoustic signals recorded in a real environment are not instantaneous but convolutive mixtures, because of the delay and the reflections. In order to separate these signals, it is effective to transform the signals into time-frequency domain. The difficult point in these approaches is the ambiguity of the pe...