Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
IEEE Transactions on Signal Processing
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
Discovering convolutive speech phones using sparseness and non-negativity
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
A two stage algorithm for K-mode convolutive nonnegative tucker decomposition
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
On connection between the convolutive and ordinary nonnegative matrix factorizations
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Real-Time speech separation by semi-supervised nonnegative matrix factorization
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Journal of Signal Processing Systems
Modelling non-stationary noise with spectral factorisation in automatic speech recognition
Computer Speech and Language
Computer Speech and Language
Information Sciences: an International Journal
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In this paper, we present a convolutive basis decomposition method and its application on simultaneous speakers separation from monophonic recordings. The model we propose is a convolutive version of the nonnegative matrix factorization algorithm. Due to the nonnegativity constraint this type of coding is very well suited for intuitively and efficiently representing magnitude spectra. We present results that reveal the nature of these basis functions and we introduce their utility in separating monophonic mixtures of known speakers