Journal
2017 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP 2017)
Volume -, Issue -, Pages 1265-1269Publisher
IEEE
DOI: 10.1109/GlobalSIP.2017.8309164
Keywords
Fully convolutional denoising autoencoders; single channel audio source separation; stacked convolutional autoencoders; deep convolutional neural networks; deep learning
Categories
Funding
- UK Engineering and Physical Sciences Research Council (EPSRC) [EP/L027119/1, EP/L027119/2]
- EPSRC [EP/L027119/2] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/L027119/2] Funding Source: researchfish
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available