Cumulative State Coherence Transform for a Robust Two-Channel Multiple Source Localization
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Adaptive harmonic spectral decomposition for multiple pitch estimation
IEEE Transactions on Audio, Speech, and Language Processing
Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
A flexible component model for precision ICA
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
A robust method to count and locate audio sources in a multichannel underdetermined mixture
IEEE Transactions on Signal Processing
The 2010 signal separation evaluation campaign (SiSEC2010): audio source separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Informed source separation through spectrogram coding and data embedding
Signal Processing
Low-Latency instrument separation in polyphonic audio using timbre models
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
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Most of audio source separation methods are developed for a particular scenario characterized by the number of sources and channels and the characteristics of the sources and the mixing process. In this paper we introduce a general modular audio source separation framework based on a library of flexible source models that enable the incorporation of prior knowledge about the characteristics of each source. First, this framework generalizes several existing audio source separation methods, while bringing a common formulation for them. Second, it allows to imagine and implement new efficient methods that were not yet reported in the literature. We first introduce the framework by describing the flexible model, explaining its generality, and summarizing our modular implementation using a Generalized Expectation-Maximization algorithm. Finally, we illustrate the above-mentioned capabilities of the framework by applying it in several new and existing configurations to different source separation scenarios.