基于改进能熵比的维纳滤波语音增强算法

为了提高低信噪比环境下语音增强的效果、算法的鲁棒性.在基于维纳滤波算法的基础上，结合基于频域特征的语音端点检查算法，提出了一种新的语音增强算法.端点检测算法使用小波包ERB子带的谱熵和改进的频域能量的能熵比法.其中，小波包ERB子带的谱熵考虑了人耳听觉掩蔽模型和语音与噪声信号之间的频率分布之间的不同；频域能量利用了有语音帧和无语音帧的能量不同.维纳滤波算法实时采集语音数据并使用新的参数来区别无语音段和有语音段，并在无语音段平滑更新噪声谱.实验结果表明，该端点检测算法能够很好的区分有语音段和无语音段，这就使得在低信噪比的情况下语音增强效果得到了提升，同时算法的鲁棒性和实时性也得到了保障.在与其他两种算法对比中，得到了更好的语音增强效果.

Speech Enhancement Algorithm Using Wiener Filtering Based on Improved Energy to Entropy Ratio

In order to achieve the improved effectiveness of speech enhancement under low-SNR circumstance and the robustness of the algorithm, this paper puts forward a new speech enhancement algorithm which is based on wiener filtering algorithm combined with speech endpoint detection algorithm on account of frequency domain features. The endpoint detection algorithm adopts the ratio between spectrum entropy for wavelet packet ERB sub-band and energy entropy for improvement of frequency domain. Therein, the spectral entropy of wavelet packet ERB sub-band takes masking properties of human auditory and the difference between speech and noise signal frequency distribution into account; the frequency-domain energy takes advantages of the energy difference between voice-frames and non- voice-frames. In addition, the wiener filtering algorithm acquits real time data and uses the new parameters to distinguish voice segment and no-voice segment where noise spectrum is updated smoothly. At last, the experimental results demonstrate that the endpoint detection algorithm can be able to effectively distinguish between speech segments and no speech segments, leading to the improvement of speech enhancement in the case of low SNR and the guarantee of robustness as well as real-time of the algorithm. In contrast with the other two algorithms, the new approach to speech enhancement has a better effect.