Audio source separation using hierarchical phase-invariant models

Authors:
Emmanuel Vincent
Affiliations:
INRIA, Centre Inria Rennes, Rennes Cedex, France
Venue:
NOLISP'09 Proceedings of the 2009 international conference on Advances in Nonlinear Speech Processing
Year:
2009

Citing 9
Cited 0

Validity of the Independence Assumption for the Separation of Instantaneous and Convolutive Mixtures of Speech and Music Sources

ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Mixtures of Gamma Priors for Non-negative Matrix Factorization Based Speech Separation

ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
A Uniform Framework for Ad-Hoc Indexes to Answer Reachability Queries on Large Graphs

DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Underdetermined Instantaneous Audio Source Separation via Local Gaussian Modeling

ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Multichannel nonnegative matrix factorization in convolutive mixtures. With application to blind audio source separation

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Two improved sparse decomposition methods for blind source separation

ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Combined Estimation of Spectral Envelopes and Sound Source Direction of Concurrent Voices by Multidimensional Statistical Filtering

IEEE Transactions on Audio, Speech, and Language Processing
Musical source separation using time-frequency source priors

IEEE Transactions on Audio, Speech, and Language Processing
Adaptation of Bayesian Models for Single-Channel Source Separation and its Application to Voice/Music Separation in Popular Songs

IEEE Transactions on Audio, Speech, and Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Audio source separation consists of analyzing a given audio recording so as to estimate the signal produced by each sound source for listening or information retrieval purposes. In the last five years, algorithms based on hierarchical phase-invariant models such as single- or multichannel hidden Markov models (HMMs) or nonnegative matrix factorization (NMF) have become popular. In this paper, we provide an overview of these models and discuss their advantages compared to established algorithms such as nongaussianity-based frequency-domain independent component analysis (FDICA) and sparse component analysis (SCA) for the separation of complex mixtures involving many sources or reverberation. We argue how hierarchical phase-invariant modeling could form the basis of future modular source separation systems.