Abstract: As many traditional de-noising methods fail in the intensive noises environment and are unadaptable in various noisy environments, a method of speech enhancement has been advanced based on dynamic Fractional Fourier Transform（FRFT） filtering. The acoustic signals are framed. The renewing methods are put in FRFT optimal disperse degree of noising speech and this method is implemented in detail. By TIMIT criterion voice and Noisex-92, the experimental results show that this algorithm can filter noise from voice availably and improve the performance of automatic speech recognition system significantly. It is proved to be robust under various noisy environments and Signal-to-Noise Ratio（SNR） conditions. This algorithm is of low computational complexity and briefness in realization.

Key words: acoustic signal, Fractional Fourier Transform（FRFT）, filtering, de-noising, auto-adaptive processing

摘要： 针对传统去噪方法在强背景噪声情况下，提取声音信号的能力变弱甚至失效与对不同噪声环境适应性差，提出了一种动态FRFT滤波声音信号语音增强方法。给出了不同语音噪声环境下FRFT最优聚散度的更新机制与具体实施方案。用TIMIT标准语音库与Noisex-92噪声库搭配，实验仿真表明，该算法能有效地去噪滤波，显著地提高语音识别系统性能，且在不同的噪声环境和信噪比条件下具有鲁棒性。算法计算代价小，简单易实现。

关键词: 声信号, 分数阶傅里叶变换（FRFT）, 滤波, 去噪, 自适应处理