刘悦, 王晓婷. 短时频域分形端点检测算法[J]. 微电子学与计算机, 2015, 32(9): 81-84,89. DOI: 10.19304/j.cnki.issn1000-7180.2015.09.016
引用本文: 刘悦, 王晓婷. 短时频域分形端点检测算法[J]. 微电子学与计算机, 2015, 32(9): 81-84,89. DOI: 10.19304/j.cnki.issn1000-7180.2015.09.016
LIU Yue, WANG Xiao-ting. A Speech Endpoint Detection Algorithm Based on Fractal in Short-term Frequency Domain[J]. Microelectronics & Computer, 2015, 32(9): 81-84,89. DOI: 10.19304/j.cnki.issn1000-7180.2015.09.016
Citation: LIU Yue, WANG Xiao-ting. A Speech Endpoint Detection Algorithm Based on Fractal in Short-term Frequency Domain[J]. Microelectronics & Computer, 2015, 32(9): 81-84,89. DOI: 10.19304/j.cnki.issn1000-7180.2015.09.016

短时频域分形端点检测算法

A Speech Endpoint Detection Algorithm Based on Fractal in Short-term Frequency Domain

  • 摘要: 为了提高分形端点检测的鲁棒性,使其适用于更多类型的噪声,提出了基于短时频域的分形端点检测算法.该算法利用了频域表征信号能量分布的特点,以及语音谐波分量的强周期、规律性,从短时频域能量分布上提取分形维数来区分语音和噪声.实验结果表明,提出的变换到短时频域提取分形维数的方法具有更好的鲁棒性,不但适用于较无规律的白噪声,还适用于包括坦克噪声在内的时域周期性和规律性较强的噪声.

     

    Abstract: In order to improve the robustness of fractal endpoint detection algorithm and make it suitable for more types of noise, This paper propose a fractal endpoint detection algorithm based on short-term frequency domain. Frequency reflects the energy distribution of signal and speech harmonic component have strong periodicity and regularity. Therefore it can extract fractal dimension from the energy distribution in the short time frequency domain to distinguish speech and noise. Experimental results show that the proposed method converted to the short-term frequency domain to extract fractal dimension has better robustness. It can effectively detect noise which has strong periodicity and regularity in the time domain, such as tanks noise.

     

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