Low Rank Tensor Completion for Recovering Missing Data in Multi-channel Audio Signal

YANG Lidong; WANG Jing; XIE Xiang; ZHAO Yi; KUANG Jingming

doi:10.11999/JEIT150589

Volume 38 Issue 2

Feb. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(2): 394-399

YANG Lidong, WANG Jing, XIE Xiang, ZHAO Yi, KUANG Jingming. Low Rank Tensor Completion for Recovering Missing Data in Multi-channel Audio Signal[J]. Journal of Electronics & Information Technology, 2016, 38(2): 394-399. doi: 10.11999/JEIT150589

Citation:

YANG Lidong, WANG Jing, XIE Xiang, ZHAO Yi, KUANG Jingming. Low Rank Tensor Completion for Recovering Missing Data in Multi-channel Audio Signal[J]. Journal of Electronics & Information Technology, 2016, 38(2): 394-399. doi: 10.11999/JEIT150589

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Low Rank Tensor Completion for Recovering Missing Data in Multi-channel Audio Signal

doi: 10.11999/JEIT150589

1.
2.
(School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China)

Funds:

The National Natural Science Foundation of China (61473041), Scientific Research Project in Colleges and Universities of Inner Mongolia (NJZY13139)

Received Date: 2015-05-18
Rev Recd Date: 2015-11-02
Publish Date: 2016-02-19

Abstract

Abstract

The data maybe miss due to problems in the acquisition, compression or transmission process of multi- channel audio signal. In order to take audiences real auditory sense, an approach of signal recovery based on low rank tensor completion is proposed. First, multi-channel audio signal is represented as a signal tensor. Second, tensor completion is formulated as a convex optimization problem. A closed form for augmented Lagrangian function is obtained via relaxation technique and separation of variables technique. At last, the audio tensor is recovered by alternating iteration. In experiments of varying number of missing entries, the comparisons show that the proposed method is more accurate than linear prediction and CANDECOMP/PARAFAC weighted optimization. The results of multiple stimuli with hidden reference and anchor indicate that low rank tensor completion method is validated for multi-channel audio signal recovery. The better auditory effects are obtained by recovered audio.
- Audio signal recovery,
- Tensor completion,
- Trace norm,
- Convex optimization

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References(22)

References

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