Underdetermined reverberant acoustic source separation using weighted full-rank nonnegative tensor models

Extended Nonnegative Tensor Factorisation Models for Musical Sound Source Separation

Recently, shift-invariant tensor factorisation algorithms have been proposed for the purposes of sound source separation of pitched musical instruments. However, in practice, existing algorithms require the use of log-frequency spectrograms to allow shift invariance in frequency which causes problems when attempting to resynthesise the separated sources. Further, it is difficult to impose harmo...

On underdetermined source separation

This paper discusses some theoritical results on underdetermined source separation i.e. when the mixing matrix is degenerate, espcially when there is more sources than observations. In this case, we show that the sources can be restored up to an arbitrary additive random vector. In the particular case of discrete sources, very relevant for digital communications, we show that this vector is cer...

Acoustic Blind Source Separation in Reverberant and Noisy Environments

Nonnegative Tensor Factorization for Directional Blind Audio Source Separation

We augment the nonnegative matrix factorization method for audio source separation with cues about directionality of sound propagation. This improves separation quality greatly and removes the need for training data, but doubles the computation.

Underdetermined Anechoic Blind Source Separation

In this paper, we address the problem of under-determined Blind Source Separation (BSS) of anechoic speech mixtures. We propose a demixing algorithm that exploits the sparsity of certain time-frequency expansions of speech signals. Our algorithm merges `-basis-pursuit with ideas based on the degenerate unmixing estimation technique (DUET) [1]. There are two main novel components to our approach...