Nonlinear Prediction with Deep Recurrent Neural Networks for Non-Blind Audio Bandwidth Extension

查看全文 作　　者：Lin [1,2,3,4]Jiang;Ruimin [1,5,6]Hu;Xiaochen [1,5,6]Wang;Weiping [1,5,6]Tu;Maosheng [1]Zhang

机构地区：[1]National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China;[2]Institute of Big Data and internet Innovation, Hunan University of Commerce, Changsha 410205, China;[3]Software College, East China University of Technology, Nanchang 330013, China;[4]Collaborative Innovation Center for Economics crime investigation and prevention technology, Jiangxi Province, Nanchang 330103,China;[5]Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan 430072, China;[6]Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China

出　　处：《China Communications》索引2018年第15卷第1期,共14页

基　　金：supported by the National Natural Science Foundation of China under Grant No. 61762005, 61231015, 61671335, 61702472, 61701194, 61761044, 61471271;National High Technology Research and Development Program of China (863 Program) under Grant No. 2015AA016306; Hubei Province Technological Innovation Major Project under Grant No. 2016AAA015;the Science Project of Education Department of Jiangxi Province under No. GJJ150585;The Opening Project of Collaborative Innovation Center for Economics Crime Investigation and Prevention Technology, Jiangxi Province, under Grant No. JXJZXTCX-025 摘　　要：Non-blind audio bandwidth extension is a standard technique within contemporary audio codecs to efficiently code audio signals at low bitrates. In existing methods, in most cases high frequencies signal is usually generated by a duplication of the corresponding low frequencies and some parameters of high frequencies. However, the perception quality of coding will significantly degrade if the correlation between high frequencies and low frequencies becomes weak. In this paper, we quantitatively analyse the correlation via computing mutual information value. The analysis results show the correlation also exists in low frequency signal of the context dependent frames besides the current frame. In order to improve the perception quality of coding, we propose a novel method of high frequency coarse spectrum generation to improve the conventional replication method. In the proposed method, the coarse high frequency spectrums are generated by a nonlinear mapping model using deep recurrent neural network. The experiments confirm that the proposed method shows better performance than the reference methods. 关键词：AUDIO CODING non-blind audiobandwidth EXTENSION context correlation deeprecurrent neural network

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Nonlinear Prediction with Deep Recurrent Neural Networks for Non-Blind Audio Bandwidth Extension

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