基于多窗谱估计和几何谱减的低信噪比语音增强方法

The Low SNR Speech Enhancement Method Based on Multi-Taper Spectrum Estimation and Spectral Subtraction of Geometric

在低信噪比条件下,传统谱减算法对语音和噪声的独立性假设会产生一定的"音乐噪声",从而降低了语音的可懂度.对此,本文提出了一种结合多窗谱估计和几何谱减的语音增强算法.本算法利用多窗谱估计带噪语音信号的功率谱,采用改进最小值控制递归平均算法实时跟踪估计噪声谱,应用几何谱减算法求解增益函数,从而恢复出精确的语音信号.通过在IEEE数据集上进行实验,并以PESQ和LSD作为评价指标,结果表明本算法在低信噪比条件下,缩短了带噪语音与纯净语音之间的频谱距离,并有效的抑制了增强语音的背景噪声,提高了语音可懂度.

The traditional spectral subtraction methods tend to produce＂music noise＂under low signal-to-noise ratio（SNR）due to the independence assumption of speech and noise.The intelligibility of speech is reduced greatly.In this paper,we proposed a speech enhancement method via integrating multi-taper spectrum（MTM）estimation and spectral subtraction of geometric（GA）.Our method employs the MTM to estimate the power spectrum of the noisy speech,and the improved minima controlled recursive average method to track the estimated noise spectrum in real time.Furthermore,the GA is used to calculate the gain function and speech signal is recovered accurately.Our method can reduce the spectral distance between the noisy speech and the clean speech under the low signal-to-noise ratio prominently,and restrain the background noise of the enhanced speech effectively.Experiments on the IEEE dataset,PESQ and LSD as the evaluation metrics,show that our method improves the speech intelligibility significantly.