Speech Separation based on Deep Belief Network
- DOI
- 10.2991/iiicec-15.2015.330How to use a DOI?
- Keywords
- speech separation; deep learning; deep belief network; restricted Boltzmann machine; autoencoder
- Abstract
Thanks to its hierarchical and generative nature, Deep Belief Network (DBN) is effective to feature representation and extraction in signal processing. In this paper, DBN is investigated and implemented to monaural speech separation. Firstly, two separate DBNs are trained to extract features from mixed noisy signals and target clean speech respectively. Subsequently, the two types of extracted features are associated together by training a BP neural network to obtain a mapping from the features of mixed signals to the features of target speech. Finally, by performing DBN and the above mapping neural network, target speech can be estimated from the input mixed signals. Experiments are conducted on different kinds of mixed signals including female/male speech mixtures, human-speech/Gaussian-noise audio mixtures, and human-speech/music audio mixtures. The PESQ scores of the extracted speech are 3.32, 2.59, and 3.42 respectively, which illustrates that the model performs well on speech separation tasks, especially on the mixed signals where the inference signals have obvious spectral structures.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Haijia Wu AU - Xiongwei Zhang AU - Liangliang Zhang AU - Xia Zou PY - 2015/03 DA - 2015/03 TI - Speech Separation based on Deep Belief Network BT - Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference PB - Atlantis Press SP - 1486 EP - 1493 SN - 2352-538X UR - https://doi.org/10.2991/iiicec-15.2015.330 DO - 10.2991/iiicec-15.2015.330 ID - Wu2015/03 ER -