Given a multi-microphone recording of an unknown number of speakers talking
concurrently, we simultaneously localize the sources and separate the individual speakers …

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

This paper describes a semi-supervised multichannel speech enhancement method that
uses clean speech data for prior training. Although multichannel nonnegative matrix …

This paper describes statistical multichannel speech enhancement based on a deep
generative model of speech spectra. Recently, deep neural networks (DNNs) have widely …

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 proposes a data-driven source localization approach under a noisy and
reverberant environment, using a newly defined feature named relative harmonic …

Abstract Machine learning (ML), a branch of artificial intelligence (AI), is an area of
computational science that is concerned with the analysis and interpretation of patterns and …

Recently, the research on ad-hoc microphone arrays with deep learning has drawn much
attention, especially in speech enhancement and separation. An ad-hoc microphone array …

This paper describes a new method for synchronizing microphones based on spectral
warping in an asynchronous microphone array. In an audio signal observed by an …

The recently-proposed deep clustering-based algorithms represent a fundamental advance
towards the single-channel multi-speaker speech sep-aration problem. These methods use …