Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications

Non-negative Tensor Factorization for Speech Enhancement

Authors
Liang He, Weiqiang Zhang, Mengnan Shi
Corresponding Author
Liang He
Available Online January 2016.
DOI
https://doi.org/10.2991/icaita-16.2016.5How to use a DOI?
Keywords
non-negative tensor factorization (NTF); speech enhancement; sound source separation
Abstract
This paper proposes an algorithm for speech enhancement by non-negative tensor factorisation. We group adjacent time-frequency matrices in the spectrograms together to form a tensor as a basic input in our algorithm. The non-negative tensor factorisation is followed to perform sound source separation between speeches and noises. The proposed strategy benefits from both short time spectral analysis and long term information. From the consideration of auditory theory and linguistics, the latter preserves the temporal dynamics information and intrinsic structure of speech, which are important for the continuity and integrity of hearing. We collected several types of real-life noises and conducted experiments on the TIMIT database. Experimental results demonstrated that the segmental signal to noise ratio (SSNR) and the perceptual evaluation of speech quality (PESQ) were significantly improved respectively.
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
Series
Advances in Intelligent Systems Research
Publication Date
January 2016
ISBN
978-94-6252-162-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/icaita-16.2016.5How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Liang He
AU  - Weiqiang Zhang
AU  - Mengnan Shi
PY  - 2016/01
DA  - 2016/01
TI  - Non-negative Tensor Factorization for Speech Enhancement
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
PB  - Atlantis Press
SP  - 18
EP  - 22
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-16.2016.5
DO  - https://doi.org/10.2991/icaita-16.2016.5
ID  - He2016/01
ER  -