基于低秩张量补全的多声道音频信号恢复方法

杨立东; 王晶; 谢湘; 赵毅; 匡镜明

doi:10.11999/JEIT150589

基于低秩张量补全的多声道音频信号恢复方法

doi: 10.11999/JEIT150589

2.
(北京理工大学信息与电子学院北京 100081) ②(内蒙古科技大学信息工程学院包头 014010)

基金项目:

国家自然科学基金(61473041)，内蒙古高校科研项目(NJZY13139)

计量
- 文章访问数: 1644
- HTML全文浏览量: 195
- PDF下载量: 600
- 被引次数: 0
出版历程
- 收稿日期: 2015-05-18
- 修回日期: 2015-11-02
- 刊出日期: 2016-02-19

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

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)

摘要

摘要: 多声道音频信号在采集、压缩、传输过程中可能造成音频数据丢失，为了确保给听众带来更真实的听觉感受，该文提出一种基于低秩张量补全的音频丢失数据恢复方法。首先，把多声道音频信号表示为一个张量；其次，把张量补全作为一个凸优化问题建模，利用松弛技术和变量分离技术得到闭合的增强拉格朗日函数；最后，通过交替迭代方法求解得到恢复的音频张量。在不同数据丢失率的实验中，通过与线性预测、加权优化的CANDECOMP /PARAFAC分解方法进行对比分析，表明利用张量补全方法具有更高的音频信号恢复精度，隐藏参考和基准的多激励测试结果也显示低秩张量补全方法能够有效地恢复多声道音频的丢失数据，从而获得更好的听觉效果。
- 音频信号恢复 /
- 张量补全 /
- 迹范数 /
- 凸优化
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

HTML全文

参考文献(22)

王磊, 周乐囡, 姬红兵, 等. 一种面向信号分类的匹配追踪新方法[J]. 电子与信息学报, 2014, 36(6): 1299-1306. doi: 10.3724/SP.J.1146.2013.00942.

WANG Lei, ZHOU Lenan, JI Hongbing, et al. A new matching pursuit algorithm for signal classification[J]. Journal of Electronics Information Technology, 2014, 36(6): 1299-1306. doi: 10.3724/SP.J.1146.2013.00942.

VASEGHI S and FRANYLING C. Restoration of old gramophone recordings[J]. AES Journal of the Audio Engineering Society, 1992, 40(10): 791-801.

高悦, 陈砚圃, 闵刚, 等. 基于线性预测分析和差分变换的语音信号压缩感知[J]. 电子与信息学报, 2012, 34(6): 1408-1413. doi: 10.3724/SP.J.1146.2011.01001.

GAO Yue, CHEN Yanpu, MIN Gang, et al. Compressed sensing of speech signals based on linear prediction coefficients and difference transformation[J]. Journal of Electronics Information Technology, 2012, 34(6): 1408-1413. doi: 10.3724/SP.J.1146.2011.01001.

COCCHI G and UNCINI A. Subbands audio signal recovering using neural nonlinear prediction[C]. Proceedings of the 2001 International Conference on Acoustics, Speech and Signal Processing (ICASSP), Salt Lake City, UT, USA, 2001: 1289-1292.

朱墨, 吴国清, 郭新毅. 基于盲解卷积的水声信号恢复技术[J].应用声学, 2011, 30(3): 177-186. doi:10.3969/j.issn. 1000- 310X.2011.03.003.

ZHU Mo, WU Guoqing, and GUO Xinyi. An underwater signal recovery technique based on blind deconvolution[J]. Journal of Applied Acoustics, 2011, 30(3): 177-186. doi: 10.3969/j.issn.1000-310X.2011.03.003.

ACAR E, DUNLAVY D M, KOLDA T G, et al. Scalable tensor factorizations with missing data[C]. Proceedings of the 10th SIAM International Conference on Data Mining, Columbus, OH, United States, 2010: 701-712.

ZHAO Qibin, ZHANG Liqing, and CICHOCKI A. Bayesian CP factorization of incomplete tensors with automatic rank determination[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 37(9): 1751-1763. doi: 10.1109/TPAMI.2015.2392756.

TAN Huachun, WU Yuankai, FENG Guangdong, et al. A new traffic prediction method based on dynamic tensor completion[J]. Procedia-Social and Behavioral Sciences, 2013, 96(8): 2431-2442. doi: 10.1016/j.sbspro.2013.08.272.

LIU Yuanyuan and SHANG Fanhua. An efficient matrix factorization method for tensor completion[J]. IEEE Signal Processing Letters, 2013, 20(4): 307-310. doi:10.1109/LSP. 2013.2245416.

刘园园. 快速低秩矩阵与张量恢复的算法研究[D]. [博士论文] ,西安电子科技大学, 2013. doi: 10.7666/d.D363665.

LIU Yuanyuan. Algorithm research of fast low-rank matrix and tensor recovery[D]. [Ph.D. dissertation], Xidian University, 2013. doi: 10.7666/d.D363665.

樊劲宇, 顾红, 苏卫民, 等. 基于张量分解的互质阵MIMO 雷达目标多参数估计方法[J]. 电子与信息学报, 2015, 37(4): 933-938. doi: 10.11999/JEIT140826.

FAN Jinyu, GU Hong, SU Weimin, et al. Co-prime MIMO radar multi-parameter estimation based on tensor decomposition[J]. Journal of Electronics Information Technology, 2015, 37(4): 933-938. doi: 10.11999/JEIT140826.

CICHOCKI A, ZDUNEK R, PHAN A H, et al. Nonnegative matrix and tensor factorizations[M]. Chichester, WS: John Wiley Sons, 2009: 28-31.

LERMAN G and ZHANG T. Robust recovery of multiple subspaces by geometric lp minimization[J]. Annals of Statistics, 2011, 39(5): 2686-2715. doi: 10.1214/11-AOS914.

CHEN Y, HSU C, and LIAO H M. Simultaneous tensor decomposition and completion using factor priors[J]. IEEE Transactions on Software Engineering, 2014, 36(3): 577-591. doi: 10.1109/TPAMI.2013.164.

RECHT B, FAZEL M, and PARRILO P. Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization[J]. SIAM Review, 2010, 52(3): 471-501.

LIU Ji, MUSIALSKI P, WONKA P, et al. Tensor completion for estimating missing values in visual data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 208-2121. doi: 10.1109/TPAMI.20125.39.

GANDY S, RECHT B, and YAMADA I. Tensor completion and low-n-rank tensor recovery via convex optimization[J]. Inverse Problems, 2011, 27(2): 25010-25028.

施引文献

资源附件(0)

访问统计