期刊文献+

基于改进谐波恢复算法的语音增强方法

Speech Enhancement Method Based on Improved Harmonic Retrieval Algorithm
下载PDF
导出
摘要 当信噪比较低时,语音信号的高次谐波部分会完全淹没在噪音中。针对该情况,提出一种基于改进谐波恢复算法的语音增强方法。对经过MMSE-LSA算法语音增强处理后的时域输出语音信号进行非线性处理,得到准周期冲激信号,并将其与原增强信号相乘,突出语音的谐波分量。实验结果表明,改进算法较好地解决了低信噪比时谐波失真的问题,相比传统谐波恢复算法能更好地改善语音高次谐波的质量。 The higher harmonic part of the speech signal is completely overwhelmed by the noise when Signal to Noise Ratio(SNR) is low. In response to this situation, a new algorithm is proposed. A quasi-periodic impulse signal is calculated based on the distorted signal processed by MMSE-LSA algorithm using a nonlinearity to regenerate harmonics. This artificial signal is then used to multiply the original enhanced signal, making the voice of the harmonic components to be prominent. Experimental results show that the algorithm is a good solution to the problem of Harmonic distortion better and is better than the traditional algorithm when improving the voice quality of high harmonics in low SNR.
出处 《计算机工程》 CAS CSCD 2012年第4期245-246,250,共3页 Computer Engineering
基金 国家自然科学基金资助项目(60572081)
关键词 语音增强 谐波恢复 非线性 低信噪比 冲激函数 低通滤波 speech enhancement harmonic retrieval nonlinearity low Signal to Noise Ratio(SNR) impulse function Low-pass Filtering(LPF)
  • 相关文献

参考文献5

  • 1Lim J S, Oppenheim A V. Enhancement and Bandwidth Com- pression of Noisy Speech[J]. Proceedings of the IEEE, 1979, 67(12): 1586-1604.
  • 2Boll S. Suppression of Acoustic Noise in Speech Using Spectral Subtraction[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1979, 27(3): 113-120.
  • 3沈赟,张丽清.基于高斯过程模型的语音增强[J].计算机工程,2010,36(5):162-164. 被引量:5
  • 4Ephraim Y, Malah D. Speech Enhancement Using a Minimum Mean-square Error Log-spectral Amplitude Estimator[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1985, 33(2): 443-445.
  • 5Plapous C, Marro C, Scalart P. Speech Enhancement Using Har- monic Regeneration[C] //Proc. of ICASSP’05. [S. l.] : IEEE Press, 2005.

二级参考文献4

  • 1Benesty J, Makino S, Chen J. Speech Enhancement[M]. Berlin, Germany: Springer, 2005.
  • 2Rassmussen C E, Williams K I. Gaussian Processes for Machine Learning[M]. Cambridge, MA, USA: MIT Press, 2006.
  • 3Park S, Choi S. Gaussian Process Regression for Voice Activity Detection and Speech Enhancemen[C]//Proc. of International Joint Conference on Neural Networks. [S. l.]: IEEE Press, 2008: 2879-2882.
  • 4Martin R. Noise Power Spectral Density Estimation Based on Optimal Smoothing and Minimum Statistics[J]. IEEE Trans. on Speech and Audio Processing, 2001, 9(5): 504-512.

共引文献4

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部