摘要
语音增强是语音信号处理的重要分支,也是语音识别,语音编码的重要的预处理环节。语音增强的目的就是从带噪语音中提取尽可能纯净的原始语音,提高信噪比,改善语音质量。本文从语音、人耳语音感知和噪声的特性入手,借鉴已有的各种语音增强算法,对语音增强的多种方法进行了研究与探讨。
首先对自适应滤波语音增强方法进行了研究,对传统的谱减算法及其增强型进行了探讨验证。然后,利用小波变换多尺度分析的特性,在小波域的各个尺度上选取不同的阈值对基本的频域谱减法进行改进。
第一,对语音增强的基本原理和方法进行了阐述,总结归纳了各种语音增强算法的思想、适用条件和存在的问题。
第二,介绍了自适应滤波语音增强的方法,将自适应遗忘因子RLS算法和IIR滤波结构相结合,得到性能提高的单通道自适应滤波语音增强算法。并通过仿真研究了相应的语音增强算法。
第三,对传统的谱减法及其增强型进行了验证,发现普通谱减算法有一定的去噪功能,但留有明显的类似流水声的“音乐噪声”;而其增强算法在调节α,β系数后可以取得比传统谱减法更高的信噪比,“音乐噪声”有一定减弱。
第四,使用小波变换对含有噪声的语音信号进行分解后,在不同尺度上,信号和噪声所引起的小波系数所占比重不同,可以在各尺度设定不同的阈值进行去噪。去噪后语音流畅,噪音非常小。改进型小波变换法针对语音信号的特点在阈值处理之前对其进行了清浊音判别。这种处理方法不但有效地提高了语音信号的信噪比,而且较为完整地保留了语音中清音的重要信息。仿真结果表明,这是一种效果明显,算法简单,易于实现的语音去噪方法。
Speech enhancement is one of the most important parts of the speech signal processing. It plays an important role in speech recognition and speech coding. The purpose of the speech enhancement is to take the pure originality speech from speech with noises and rise the SNR, Then improve speech quality. This thesis introduces the different characteristics between speech, hearing and noise, discuss various speech enhancement methods, and do some research on the traditional spectral subtraction method.
In this paper, an adaptive filtering in speech enhancement and the traditional spectral subtraction method are discussed. According to the theory of wavelet transform and the idea of spectral subtraction, a method on wavelet domain is presented in this thesis.
Firstly, this paper concentrates on the basic principle and methods of speech enhancement, and discusses the theories, conditions and problems about several methods of speech enhancement.
Secondly, this paper introduces the adaptive filter in speech enhancement, and combine RLS algorithm with IIR filter, then the better method of single channel adaptive filter in speech enhancement is introduced. Through simulation this paper identifies the speech enhancement algorithm.
Thirdly, the traditional spectral subtraction method is validated, found that common spectral subtraction method can filter a little noise, but has "music noise". On the other hand the enhanced method gets better SNR than traditional spectral subtraction method after it adjusts the parametersαandβ. The "music noise" is reduced.
Fourthly, after decompounding the speech signal with noise through wavelet transform. The proportions are different between signal and noise on different measures. So through setting different threshold, noises are reduced. The speech after reducing noise is fluent. Before calculating threshold, the improved wavelet transform distinguishes surd and sonant depend on characteristics of speech signal. This method not only improved speech signal's SNR availably but also held surd's important information perfectly. The simulation results indicate the method is effective. The algorithm is simple and easy to realize.
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