Blind Spectral-GMM Estimation for Underdetermined Instantaneous Audio Source Separation
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
On the improvement of singing voice separation for monaural recordings using the MIR-1K dataset
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
Source/filter model for unsupervised main melody extraction from polyphonic audio signals
IEEE Transactions on Audio, Speech, and Language Processing
A method for impact noise reduction from speech using a stationary-nonstationary separating filter
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
Pattern induction and matching in music signals
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
Single-Channel Source Separation of Audio Signals Using Bark Scale Wavelet Packet Decomposition
Journal of Signal Processing Systems
Audio source separation using hierarchical phase-invariant models
NOLISP'09 Proceedings of the 2009 international conference on Advances in Nonlinear Speech Processing
Uncertainty-based learning of acoustic models from noisy data
Computer Speech and Language
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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Probabilistic approaches can offer satisfactory solutions to source separation with a single channel, provided that the models of the sources match accurately the statistical properties of the mixed signals. However, it is not always possible to train such models. To overcome this problem, we propose to resort to an adaptation scheme for adjusting the source models with respect to the actual properties of the signals observed in the mix. In this paper, we introduce a general formalism for source model adaptation which is expressed in the framework of Bayesian models. Particular cases of the proposed approach are then investigated experimentally on the problem of separating voice from music in popular songs. The obtained results show that an adaptation scheme can improve consistently and significantly the separation performance in comparison with nonadapted models.