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Sound Source Separation of Overcomplete Convolutive Mixtures Using Generalized Sparseness
- Author(s)
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- Masahito Togami, (Central Research Laboratory, Hitachi, Ltd)
- Takashi Sumiyoshi, (Central Research Laboratory, Hitachi, Ltd)
- Akio Amano, (Central Research Laboratory, Hitachi, Ltd)
- Topics
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- Sound enhancement and sound separation
- Adaptive filtering algorithms and structures for echo and noise control
- Noise reduction techniques
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Abstract
We propose a sound source separation method that works well even if there are more sources than mixtures and signals are recorded in a reverberant room. The proposed method is based on generalized sparseness, where the number of active sources is assumed to vary from 1 to the number of mixtures M at each time-frequency point, and the proposed sparseness estimater estimates the most suitable number of active sources. Experimental results in a room (reverberation time = 100 ms) indicate that signals separated by our proposed method outperform those by binary masks and the shortest-path algorithm by about 3-5db.
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