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• W3016107658 abstract "Discriminative models for source separation have recently been shown to produce impressive results. However, when operating on sources outside of the training set, these models can not perform as well and are cumbersome to update. Classical methods like Nonnegative Matrix Factorization (NMF) provide modular approaches to source separation that can be easily updated to adapt to new mixture scenarios. In this paper, we generalize NMF to develop end-to-end non-negative auto-encoders and demonstrate how they can be used for source separation. Our experiments indicate that these models deliver comparable separation performance to discriminative approaches, while retaining the modularity of NMF and the modeling flexibility of neural networks." @default.
• W3016107658 created "2020-04-17" @default.
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• W3016107658 date "2020-05-01" @default.
• W3016107658 modified "2023-09-25" @default.
• W3016107658 title "End-To-End Non-Negative Autoencoders for Sound Source Separation" @default.
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• W3016107658 doi "https://doi.org/10.1109/icassp40776.2020.9053588" @default.
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