This paper describes several important methods for the blind source separation of audio
signals in an integrated manner. Two historically developed routes are featured. One started …

Popular music is often composed of an accompaniment and a lead component, the latter
typically consisting of vocals. Filtering such mixtures to extract one or both components has …

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

This paper addresses the determined blind source separation problem and proposes a new
effective method unifying independent vector analysis (IVA) and nonnegative matrix …

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

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 …

Recently, a generative variational autoencoder (VAE) has been proposed for speech
enhancement to model speech statistics. However, this approach only uses clean speech in …

In this paper, we propose a new framework called independent deeply learned matrix
analysis (IDLMA), which unifies a deep neural network (DNN) and independence-based …

In this paper we address speaker-independent multichannel speech enhancement in
unknown noisy environments. Our work is based on a well-established multichannel local …

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 …