Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (5): 150-155.DOI: 10.3778/j.issn.1002-8331.1705-0337

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Speech enhancement algorithm of sparse low rank model and phase spectral compensation

WANG Hu, LI Jing, ZHAO Hengmiao, ZANG Yan, LI Chuntang   

  1. College of Electronic, Communication and Physics, Shandong University of Science and Technology, Qingdao, Shandong 266590, China
  • Online:2018-03-01 Published:2018-03-13


王  虎,李  晶,赵恒淼,臧  燕,李春堂   

  1. 山东科技大学 电子通信与物理学院,山东 青岛 266590

Abstract: Aiming at the problems that the existing speech enhancement algorithms have poor speech enhancement effect and speech signal distortion, a speech enhancement algorithm with sparse low rank model and improved phase spectrum compensation is proposed. First, the sparse low rank model is used to deal with the amplitude spectrum of the noisy speech, and the separated speech is obtained. Second, Normalized Least Mean Square(NLMS) adaptive filtering algorithm is applied to optimize the compensation factor of Phase Spectrum Compensation(PSC) algorithm. In addition, speech after the sparse low rank separation is processed by the improved phase spectrum compensation to obtain the enhanced speech. Finally, the Perceptual Evaluation of Speech Quality(PESQ) and spectrum are used to analyze the enhanced speech. The experimental results show that the noise can be effectively removed with the method, and in the case of low signal to noise ratio, the clarity of speech can be maintained with the method.

Key words: speech enhancement, sparse low rank model, phase spectrum compensation, Normalized Least Mean Square(NLMS), perceptual evaluation of speech quality, compensation factor

摘要: 针对现有的语音增强算法存在增强效果差、语音信号失真等问题,提出了稀疏低秩模型及改进型相位谱补偿的语音增强算法。首先,用稀疏低秩模型处理含噪语音的幅度谱,得到分离后的语音。接着,用归一化最小均方自适应滤波算法优化相位谱补偿算法的补偿因子。然后,对稀疏低秩分离后的语音进行改进型相位谱补偿处理,得到最终增强的语音。最后,对增强后的语音进行感知语音质量评价分析及频谱分析。实验结果表明,该方法能够有效地去除噪声,并且在低信噪比的情况下,可以保持语音的清晰度。

关键词: 语音增强, 稀疏低秩模型, 相位谱补偿, 归一化最小均方(NLMS), 感知语音质量评价, 补偿因子