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• W2940948712 abstract "We address talker-independent monaural speaker separation from the perspectives of deep learning and computational auditory scene analysis (CASA). Specifically, we decompose the multi-speaker separation task into the stages of simultaneous grouping and sequential grouping. Simultaneous grouping is first performed in each time frame by separating the spectra of different speakers with a permutation-invariantly trained neural network. In the second stage, the frame-level separated spectra are sequentially grouped to different speakers by a clustering network. The proposed deep CASA approach optimizes frame-level separation and speaker tracking in turn, and produces excellent results for both objectives. Experimental results on the benchmark WSJ0-2mix database show that the new approach achieves the state-of-the-art results with a modest model size." @default.
• W2940948712 created "2019-05-03" @default.
• W2940948712 creator A5051837453 @default.
• W2940948712 creator A5058713341 @default.
• W2940948712 date "2019-04-24" @default.
• W2940948712 modified "2023-09-24" @default.
• W2940948712 title "Divide and Conquer: A Deep CASA Approach to Talker-independent Monaural Speaker Separation" @default.
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• W2940948712 doi "https://doi.org/10.48550/arxiv.1904.11148" @default.
• W2940948712 hasPublicationYear "2019" @default.
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