Non-negative matrix factorization with α-divergence
Pattern Recognition Letters
A geometric approach to spectral subtraction
Speech Communication
Discovering convolutive speech phones using sparseness and non-negativity
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Convolutive Speech Bases and Their Application to Supervised Speech Separation
IEEE Transactions on Audio, Speech, and Language Processing
Soft Mask Methods for Single-Channel Speaker Separation
IEEE Transactions on Audio, Speech, and Language Processing
Evaluation of Objective Quality Measures for Speech Enhancement
IEEE Transactions on Audio, Speech, and Language Processing
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We introduce a non-negative matrix factorization technique which learns speech features with temporal extent in the presence of non-stationary noise. Our proposed technique, namely Sparse convolutive robust non-negative matrix factorization, is robust in the presence of noise due to our explicit treatment of noise as an interfering source in the factorization. We derive multiplicative update rules using the alpha divergence objective. We show that our proposed method yields superior performance to sparse convolutive non-negative matrix factorization in a feature learning task on noisy data and comparable results to dedicated speech enhancement techniques.