Journal of Guangdong University of Technology ›› 2018, Vol. 35 ›› Issue (03): 29-36.doi: 10.12052/gdutxb.180043

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An Incremental Learning Approach in Voice Compression via Sparse Dictionary Learning

Teng Shao-hua, Song Huan, Huo Ying-xiang, Zhang Wei   

  1. School of Computers, Guangdong University of Technology, Guangzhou, 510006, China
  • Received:2018-03-07 Online:2018-05-09 Published:2018-04-26
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Abstract: The explosive growth of audio streams brings difficulties in storage and transmission; however, many methods could not give high compression ratio while keeping the quality. In order to solve this problem, the proposed method compresses amplitude spectrum of voice by constructing a dynamic sparse voice dictionary based on incremental learning. It calculates amplitude envelopes spectrums via Short-Time Fourier Transform (STFT) firstly, and then it uses a dictionary to fit each envelope by projecting high dimensional vectors to several 2D planes. In addition, it minimizes the number of dictionary items and therefore can store the parameters of linear interpolation instead of spectrums. Otherwise, if the fitting step above fails, it will store this window of spectrum directly. By using dictionary and parameters of linear interpolation, it can reconstruct the spectrum efficiently in decompressing process. The results of experiments show that comparing with other methods, the proposed method gives high compression ratio as well as better accuracy in decompressing, and adapt to live voice stream encoding with high sampling rate.

Key words: voice compression, voice decompression, real-time processing, streaming data, incremental learning, sparse dictionary learning

CLC Number: 

  • TP301
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[1] YANG Ting, TENG Shao-Hua. Research and Application of Improved Bayes Algorithm for the Telecommunication Customer Churn [J]. Journal of Guangdong University of Technology, 2015, 32(3): 67-72.
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