Speech enhancement based on nonnegative matrix factorization with mixed group sparsity constraint

Hien-Thanh Duong 1, 2 Quoc-Cuong Nguyen 2, 3 Cong-Phuong Nguyen 3 Thanh-Huan Tran 4 Ngoc Q. K. Duong 5
1 HUMG - Hanoi University of Mining and Geology
2 MICA - International Research Institute MICA
3 HUST - Hanoi University of Science and Technology
4 Hanoi University of Industry
5 Technicolor R & I [Cesson Sévigné]
Abstract : This paper addresses a challenging single-channel speech enhancement problem in real-world environment where speech signal is corrupted by high level background noise. While most state-of-the-art algorithms tries to estimate noise spectral power and filter it from the observed one to obtain enhanced speech, the paper discloses another approach inspired from audio source separation technique. In the considered method, generic spectral characteristics of speech and noise are first learned from various training signals by non-negative matrix factorization (NMF). They are then used to guide the similar factorization of the observed power spectrogram into speech part and noise part. Additionally, we propose to combine two existing group sparsity-inducing penalties in the optimization process and adapt the corresponding algorithm for parameter estimation based on mul-tiplicative update (MU) rule. Experiment results over different settings confirm the effectiveness of the proposed approach .
Keywords : spectral model group sparsity Speech enhancement audio source separation nonnegative matrix factorization multiplicative update
Type de document :
Communication dans un congrès
6th ACM International Symposium on Information and Communication Technology, Dec 2015, Hanoi, Vietnam. 〈10.1145/2833258.2833276〉
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Hien-Thanh Duong, Quoc-Cuong Nguyen, Cong-Phuong Nguyen, Thanh-Huan Tran, Ngoc Q. K. Duong. Speech enhancement based on nonnegative matrix factorization with mixed group sparsity constraint. 6th ACM International Symposium on Information and Communication Technology, Dec 2015, Hanoi, Vietnam. 〈10.1145/2833258.2833276〉. 〈hal-01288291〉

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