Single channel audio source separation using convolutional denoising autoencoders

Deep learning techniques have been used recently to tackle the audio source separation problem. In this work, we propose to use deep fully convolutional denoising autoencoders (CDAEs) for monaural audio source separation. We use as many CDAEs as the number of sources to be separated from the mixed s...

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Veröffentlicht in:	2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP) S. 1265 - 1269
Hauptverfasser:	Grais, Emad M., Plumbley, Mark D.
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.11.2017
Schlagworte:	Convolution Convolutional codes deep convolutional neural networks deep learning Feature extraction Fully convolutional denoising autoencoders Noise reduction single channel audio source separation Source separation Spectrogram stacked convolutional autoencoders Two dimensional displays
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Zusammenfassung:	Deep learning techniques have been used recently to tackle the audio source separation problem. In this work, we propose to use deep fully convolutional denoising autoencoders (CDAEs) for monaural audio source separation. We use as many CDAEs as the number of sources to be separated from the mixed signal. Each CDAE is trained to separate one source and treats the other sources as background noise. The main idea is to allow each CDAE to learn suitable spectral-temporal filters and features to its corresponding source. Our experimental results show that CDAEs perform source separation slightly better than the deep feedforward neural networks (FNNs) even with fewer parameters than FNNs.
DOI:	10.1109/GlobalSIP.2017.8309164