This article describes a computationally-efficient blind source separation (BSS) method
based on the independence, low-rankness, and directivity of the sources. A typical approach …

Multi-channel signals captured by spatially separated sensors often contain a high level of
data redundancy. A compact signal representation enables more efficient storage and …

Blind source separation (BSS) of audio signals aims to separate original source signals from
their mixtures recorded by microphones. The applications include automatic speech …

This paper describes multichannel speech enhancement for improving automatic speech
recognition (ASR) in noisy environments. Recently, the minimum variance distortionless …

In this paper, we address the problem of extracting all super-Gaussian source signals from a
linear mixture in which (i) the number of super-Gaussian sources K is less than that of …

This article describes a computationally-efficient statistical approach to joint (semi-) blind
source separation and dereverberation for multichannel noisy reverberant mixture signals. A …

Multichannel blind audio source separation aims to recover the latent sources from their
multichannel mixtures without supervised information. One state-of-the-art blind audio …

This paper describes a joint blind source separation and dereverberation method that works
adaptively and efficiently in a reverberant noisy environment. The modern approach to blind …

In this paper, we address signal enhancement in underdetermined situations and propose
new beamforming algorithms. Beamforming in (over) determined situations can successfully …

Overlapping speech and high room reverberation deteriorate the accuracy of automatic
speech recognition (ASR). This paper proposes a method for jointly optimum source …