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Nonlinear Prediction with Deep Recurrent Neural Networks for Non-Blind Audio Bandwidth Extension 被引量:2
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作者 Lin Jiang Ruimin Hu +2 位作者 Xiaochen Wang Weiping Tu Maosheng Zhang 《China Communications》 SCIE CSCD 2018年第1期72-85,共14页
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 gen... 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. 展开更多
关键词 AUDIO CODING non-blind audiobandwidth EXTENSION context correlation deeprecurrent neural network
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