Abstract: Multi-spectral entropy not only has the same frequency characteristics with the spectral entropy, but also can show the distribution of energy. Therefore, while conducting the voice detection, it tends to use the multi-spectral entropy to estimate. This paper proves that using multi-spectral entropy to estimate has the advantage over the spectral entropy in the non-stationary signal detection, and properly selects the multi-spectral entropy noise estimation algorithm which is suitable for tank environment. Combining multi-spectral entropy estimation, related weighted, sub-frame subtraction and other theories, it puts forward an algorithm of speech enhancement, based on multiple-window spectrum estimation. Simulation results show that the algorithm can not only suppress the background and musical noises better, but also keep a good intelligibility and naturalness of the voice.

Key words: speech enhancement, multi-spectral entropy estimation, multi-window spectral estimation, correlative weighting, sub-frame subtraction

摘要： 多带谱熵不仅能体现和谱熵一样的频率特性，还能体现能量的分布情况，因此在进语音检测时更趋向于采用多带谱熵估计。通过仿真，证明多带谱熵估计在非平稳信号检测中相比于谱熵估计的优越性，确定适合坦克环境的多带谱熵噪声估计算法。结合多带谱熵估计、相关加权、分帧相减等理论，提出一种以多窗谱估计为基础的改进的语音增强算法。仿真结果表明，提出的算法不仅能更好地抑制背景噪声和音乐噪声，而且还较好地保持了语音的可懂度和自然度。

关键词: 语音增强, 多带谱熵估计, 多窗谱估计, 相关加权, 分帧相减