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应用科学学报 ›› 2013, Vol. 31 ›› Issue (6): 579-584.doi: 10.3969/j.issn.0255-8297.2013.06.005

• 信号与信息处理 • 上一篇    下一篇

低信噪比环境下语音检测的邻域极值差分信号功率谱分维算法

陈雪勤1,2， 俞一彪1， 赵鹤鸣1   

  1. 1. 苏州大学电子信息学院，江苏苏州215006
    2. 北京交通大学现代信息科学与网络技术北京市重点实验室，北京100044
  • 收稿日期:2012-10-11 修回日期:2013-01-27 出版日期:2013-11-29 发布日期:2013-01-27
  • 作者简介:陈雪勤，博士，讲师，研究方向：语音信号处理，E-mail: chenxueqin@suda.edu.cn
  • 基金资助:

    国家自然科学基金(No. 61071215,No. 61271360)；江苏省自然科学基金(No. BK20131196)；苏州大学预研项目基金(No.
    Q311901111, No. 14317399)资助

Algorithm of Fractal Dimension Based on Neighborhood Extremum Difference Signal Power Spectrum with Application to Low SNR Speech Activity Detection

CHEN Xue-qin1,2, YU Yi-biao1, ZHAO He-ming1   

  1. 1. School of Electronics and Information Engineering, Soochow University, Suzhou 215006, Jiangsu Province, China
    2. The Key Laboratory of Advanced Information Science and Network Technology of Beijing, Beijing Jiaotong University, Beijing 100044, China
  • Received:2012-10-11 Revised:2013-01-27 Online:2013-11-29 Published:2013-01-27

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1299

被引次数

1

摘要： 提出一种邻域极值差分信号功率谱的分形维值算法，并用于低信噪比环境下的语音活动检测. 在时域信号邻域范围内作极值差分检索获得邻域极值差分信号，进一步根据差分信号功率谱估计的最小误差求解分维值.在安静环境下，对正常语音和耳语音的语音信号活动检测(speech activity detection, SAD)性能与盒维相似，明显好于谱熵算法. 多种噪声环境下的SAD检测结果显示，所提算法的误检率远低于谱熵算法，在除白噪声以外各种条件下的误检率均低于盒维算法，且计算量约为盒维算法的5%. 实验表明，该算法在SAD检测和效率两方面具有良好的综合性能.

关键词: 语音活动检测, 低信噪比, 分形维, 功率谱

Abstract: In this paper, a fractal dimension algorithm is proposed based on the neighborhood extremum difference signal and its power spectrum. The proposed method is applied to speech activity detection (SAD)in low SNR environments. In the time domain, the extremum difference signal is searched in the neighborhood.The fractal value is then estimated from the power spectrum of the difference signal based on a minimum error criterion. In a quiet environment, performance of the method is similar to the box algorithm and better than entropy algorithm in normal and whispered speech detection, while in several noise environments, it clearly outperforms the entropy algorithm. It is also better than the box algorithm except in a white noise
environment. In addition, the computation load is only 5% of the box algorithm. Experimental results show that the proposed algorithm has a good overall performance in terms of efficiency and SAD.

Key words: speech activity detection, low SNR, fractal dimension, power spectrum

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