计算机工程

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基于鲁棒主成分分析的音乐信号降噪

刘迪,关欣,李锵,滕建辅   

  1. (天津大学 电子信息工程学院,天津 300072)
  • 收稿日期:2015-09-14 出版日期:2016-09-15 发布日期:2016-09-15
  • 作者简介:刘迪(1995-),女,硕士研究生,主研方向为音频信号处理;关欣,讲师;李锵、滕建辅,教授。
  • 基金项目:
    国家自然科学基金资助项目(60802049,61101225)。

Music Signal De-noising Based on Robust Principal Component Analysis

LIU Di,GUAN Xin,LI Qiang,TENG Jianfu   

  1. (School of Electronic Information Engineering,Tianjin University,Tianjin 300072,China)
  • Received:2015-09-14 Online:2016-09-15 Published:2016-09-15

摘要: 为提高生活场景下录制音乐的质量,提出一种改进的音乐信号降噪方法。根据独奏音乐信号的低秩特征对含噪音乐信号进行噪声位置检测的预处理,将鲁棒主成分分析(RPCA)应用于音乐降噪。选择增广拉格朗日乘子法解决RPCA优化问题,引入信噪比(SNR)和音频质量感知评价(PEAQ)标准作为评价指标,并与小波降噪和独立主成分分析降噪方法进行对比实验,结果表明RPCA降噪方法可以使音乐信号的SNR提高约2 dB~5 dB,PEAQ也得到一定程度的提升,具有较好的独奏音乐降噪效果。

关键词: 音乐降噪, 鲁棒主成分分析, 非高斯噪声, 低秩矩阵恢复, 音频质量感知评价标准

Abstract: In order to improve the quality of recording music in the life scene,this paper presents an improved music signal de-noising method.The music containing noise is preprocessed based on the low-rank characteristic of solo music.The Robust Principal Component Analysis(RPCA) is applied in the field of music de-noising,and the augmented lagrange multiplier method is selected to solve the optimization problem of RPCA.The comparison with wavelet de-noising and Independent Principal Component Analysis(IPCA) de-noising is made.Perceptual Evaluation of Audio Quality(PEAQ) and Signal to Noise Ratio(SNR) are introduced as the evaluation indexs of comparison experiments.Experimental results show that the SNR of the music signal can be improved by about 2 dB~5 dB,and PEAQ of the music is also improved.The proposed method has good de-noising effect for solo music.

Key words: music de-noising, Robust Principal Component Analysis(RPCA), non-Gaussion noise, low-rank matrix recovery, Perceptual Evaluation of Audio Quality(PEAQ) standard

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