• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 人工智能与数据挖掘 • 上一篇    下一篇

基于计算听觉场景分析的单声道浊音分离

张丽娜,张二华,江军亮   

  1. (南京理工大学计算机科学与工程学院,江苏 南京 210094)
  • 收稿日期:2018-05-10 修回日期:2018-11-08 出版日期:2019-07-25 发布日期:2019-07-25
  • 基金资助:

    军委装备发展部“十三五”装备预研领域基金(61403120102)

Monaural voiced speech separation based
on computational auditory scene analysis

ZHANG Lina,ZHANG Erhua,JIANG Junliang   

  1. (School of Computer Science and Engineering,Nanjing University of Science & Technology,Nanjing 210094,China)
     
  • Received:2018-05-10 Revised:2018-11-08 Online:2019-07-25 Published:2019-07-25

摘要:

针对单声道语音分离中浊音分离的问题,提出了一种准确估计基音周期的方法。首先,以语音的短时平稳性和基音周期的连续性等为线索,利用语音信号的倒谱峰值构成基音周期谱图,并自动提取基音周期轨迹。然后,利用谐波频率为基音频率整数倍的性质来拾取各次谐波的频谱。最后,通过傅里叶逆变换对浊音进行重构。实验结果表明,该方法能准确提取基音周期轨迹,有效分离浊音信号。

 

关键词: 计算听觉场景分析, 语音分离, 基音周期轨迹, 浊音

Abstract:

Aiming at the problem of voiced speech separation in monaural speech separation,  we propose an accurate pitch period estimation method. Firstly, using the shortterm stability of speech and the continuity of the pitch period as clues, we use the cepstrum peak of speech signals to form the pitch spectrum, and the pitch period track is automatically extracted. Then, the spectrum of each harmonic is picked up by using the property that the harmonic frequency is an integer multiple of the fundamental frequency. Finally, the voiced speech is reconstructed by the inverse Fourier transform. Experimental results show that this method can accurately extract the pitch period track and effectively separate voiced signals.

 

 

 

Key words: computational auditory scene analysis, speech separation, pitch periodic track, voiced speech