Single-channel, speaker-independent speech separation methods have recently seen great
progress. However, the accuracy, latency, and computational cost of such methods remain …

Generative AI has demonstrated impressive performance in various fields, among which
speech synthesis is an interesting direction. With the diffusion model as the most popular …

A new algorithm is proposed for robust principal component analysis with predefined
sparsity patterns. The algorithm is then applied to separate the singing voice from the …

Non-negative matrix factorization (NMF) is a powerful tool for data science researchers, and
it has been successfully applied to data mining and machine learning community, due to its …

In recent years, nonnegative matrix factorization (NMF) with volume regularization has been
shown to be a powerful identifiable model; for example for hyperspectral unmixing …

This paper presents a robust variant of nonnegative matrix factorization (NMF) based on
complex Student's t distributions (t-NMF) for source separation of single-channel audio …

Abstract Blind Source Separation (BSS) of complex signals composed of radar,
communication and jamming signals is the first step in an integrated electronic system …

This work presents self-supervised learning methods for monaural speaker-specific (ie,
personalized) speech enhancement models. While general-purpose models must broadly …

This paper describes a versatile tensor factorization technique called independent low-rank
tensor analysis (ILRTA) and its application to single-channel audio source separation. In …

This paper presents an ultimate extension of nonnegative matrix factorization (NMF) for
audio source separation based on full covariance modeling over all the time-frequency (TF) …