  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.
