Single Channel Source Separation Using Filterbank and 2D Sparse Matrix Factorization

Non-Negative Matrix Factorization with Sparsity Learning for Single Channel Audio Source Separation

Single-channel Speech Separation Using Orthogonal Matching Pursuit

In this paper, we propose a new sparse decomposition based single-channel speech separation method using orthogonal matching pursuit (OMP). The separation is performed using source-individual dictionaries consisting of time-domain training frames as atoms. OMP is used to compute sparse coefficients to estimate sources. We report the separation results of our proposed method and compare them wit...

Nonnegative Tensor Factorization with Frequency Modulation Cues for Blind Audio Source Separation

We present Vibrato Nonnegative Tensor Factorization, an algorithm for single-channel unsupervised audio source separation with an application to separating instrumental or vocal sources with nonstationary pitch from music recordings. Our approach extends Nonnegative Matrix Factorization for audio modeling by including local estimates of frequency modulation as cues in the separation. This permi...

Bayesian Group Sparse Learning for Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) is developed for parts-based representation of nonnegative data with the sparseness constraint. The degree of sparseness plays an important role for model regularization. This paper presents Bayesian group sparse learning for NMF and applies it for single-channel source separation. This method establishes the common bases and individual bases to characteri...

Bayesian group sparse learning for music source separation

Nonnegative matrix factorization (NMF) is developed for parts-based representation of nonnegative signals with the sparseness constraint. The signals are adequately represented by a set of basis vectors and the corresponding weight parameters. NMF has been successfully applied for blind source separation and many other signal processing systems. Typically, controlling the degree of sparseness a...