Speech enhancement and separation are core problems in audio signal processing, with
commercial applications in devices as diverse as mobile phones, conference call systems …

We consider inference in a general data-driven object-based model of multichannel audio
data, assumed generated as a possibly underdetermined convolutive mixture of source …

This paper addresses the modeling of reverberant recording environments in the context of
under-determined convolutive blind source separation. We model the contribution of each …

Most audio source separation methods are developed for a particular scenario
characterized by the number of sources and channels and the characteristics of the sources …

This letter proposes a multichannel source separation technique, the multichannel
variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model …

In various signal processing applications, such as audio signal recovery, the extraction of
desired signals from a mixture of other signals is a crucial task. To achieve superior …

In the recent years, many studies have focused on the single-sensor separation of
independent waveforms using so-called soft-masking strategies, where the short term …

In this chapter, we address the determined blind source separation problem and introduce a
new effective method of unifying independent vector analysis (IVA) and nonnegative matrix …

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

In the contemporary era, blind source separation has emerged as a highly appealing and
significant research topic within the field of signal processing. The imperative for the …