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- W2057935420 abstract "This paper seeks to exploit high-level temporal information during feature extraction from audio signals via non-negative matrix factorization. Contrary to existing approaches that impose local temporal constraints, we train powerful recurrent neural network models to capture long-term temporal dependencies and event co-occurrence in the data. This gives our method the ability to “fill in the blanks” in a smart way during feature extraction from complex audio mixtures, an ability very useful for a number of audio applications. We apply these ideas to source separation problems." @default.
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- W2057935420 date "2014-05-01" @default.
- W2057935420 modified "2023-10-03" @default.
- W2057935420 title "Exploiting long-term temporal dependencies in NMF using recurrent neural networks with application to source separation" @default.
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- W2057935420 doi "https://doi.org/10.1109/icassp.2014.6854951" @default.
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