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a The NN structure for audio source separation task.
Fig. 6 Reconstructed spectrogram of audio source separation task.
It is intuitive that in audio source separation task, the time duration of the filters is much smaller than in audio scene classification task, especially at low frequencies.
For the speech signals, we used the monaural dry speech sources from the source separation task in SiSEC2011 [56] whose data names are dev1_female4 and dev1_male4 [57].
However, for audio source separation task, the modules will be composed by a binary gating layer and some spectrogram reconstruction layers.
In this experiment, we investigate the application of long-term filter banks in audio source separation task using the MIR-1K dataset [46].
The spectrogram reconstruction error in audio source separation task is reduced by relatively 6.7% and the classification error in audio scene classification task is reduced by relatively 6.5%, when compared with the traditional frequency filter banks.
In the case of musical signals, a possible sound source separation task would be to obtain independent signals for the saxophone, piano, bass, and percussion, given a recording or audio mixture of a jazz quartet.
In the experiment of audio source separation task, when the parameters of totally independent long-term filter banks are initialized randomly, the result seems to be unable to converge effectively.
Other than the biological signals that are occurring at the cell or organ level, there are also various source separation tasks that we consciously do daily.
Source separation is a common task in signal processing and is often analogous to factor analysis.
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