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Cracking the cocktail party problem by multi-beam deep attractor network
[article]
2018
arXiv
pre-print
While recent progresses in neural network approaches to single-channel speech separation, or more generally the cocktail party problem, achieved significant improvement, their performance for complex mixtures is still not satisfactory. In this work, we propose a novel multi-channel framework for multi-talker separation. In the proposed model, an input multi-channel mixture signal is firstly converted to a set of beamformed signals using fixed beam patterns. For this beamforming, we propose to
arXiv:1803.10924v1
fatcat:tfijy4ujn5cjxjcuga73mhcvci