Convolutive Non-Negative Matrix Factorization for CQT Transform using Itakura-Saito Divergence

Fabio Louvatti do Carmo, Evandro Ottoni Teatini Salles

DOI: 10.14209/sbrt.2017.29

Evento: XXXV Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2017)

Keywords: Non-negative Matrix Factorization Monaural separation Blind source separation Constant-Q Transform Itakura-Saito

Abstract

This paper proposes a modification of the Nonnegative Matrix Factorization (NMF) in a single-channel audio source separation problem. NMF is widely used in such problem because of its easy implementation and parts-based separation properties. However, the original NMF uses Short Time Fourier Transform (STFT) as a spectral representation of the data, which has matrix representation, and it does not support data at nonregular grid, such as Constant-Q Transform (CQT). CQT has a strong appeal in audio processing because it approximates the human auditory system in a reasonably way. Besides to usage of CQT as spectral representation, this paper presents a convolutive NMF approach using Itakura-Saito divergence (ISD) to work with irregularly-sampled data, here defined as NRCNMF-IS. The scale invariance property of ISD is interesting for audio applications. The NRCNMF-IS was tested and compared with its matricial version. Utilizing performance metrics, the statistical results show that the use of CQT as spectral representation yields better results than the STFT representation.

Download