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 …

Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …

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 …

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 …

We propose a new algorithm for the blind source separation of acoustic sources. This
algorithm is an alternative to the popular auxiliary function based independent vector …

In recent years, deep learning has achieved great success in speech enhancement.
However, there are two major limitations regarding existing works. First, the Bayesian …

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 …

This paper describes aneural blind source separation (BSS) method based on amortized
variational inference (AVI) of a non-linear generative model of mixture signals. A classical …