Blind Audio Source Separation with Sparse Nonnegative Matrix Factorization
| Autor: | Shakir Saat, Nor Zaidi Haron, Abd Majid Darsono, N. A. Manap, M. M. Ibrahim |
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| Rok vydání: | 2014 |
| Předmět: |
General Computer Science
Basis (linear algebra) business.industry Multiplicative function General Engineering Pattern recognition Function (mathematics) Blind signal separation Convolution Non-negative matrix factorization Joint probability distribution Source separation Artificial intelligence business Mathematics |
| Zdroj: | Research Journal of Applied Sciences, Engineering and Technology. 7:5015-5020 |
| ISSN: | 2040-7467 2040-7459 |
| DOI: | 10.19026/rjaset.7.894 |
| Popis: | In this study, a new technique in source separation using Two-Dimensional Nonnegative Matrix Factorization (NMF2D) with the Beta-divergence is proposed. The Time-Frequency (TF) profile of each source is modeled as two-dimensional convolution of the temporal code and the spectral basis. In addition, adaptive sparsity constraint was imposed to reduce the ambiguity and provide uniqueness to the solution. The proposed model used Beta-divergence as a cost function and updated by maximizing the joint probability of the mixing spectral basis and temporal codes using the multiplicative update rules. Experimental tests have been conducted in audio application to blindly separate the source in musical mixture. Results have shown the effectiveness of the algorithm in separating the audio sources from single channel mixture. |
| Databáze: | OpenAIRE |
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