基于小波系数统计的非高斯噪声背景下语音流检测

在实际生活中,非高斯噪声很普遍,对信号的影响也很大,是语音信号处理中的难题.大部分强噪声信号都是非高斯的,在强噪声背景下,由于语音信号受到较大的干扰,甚至被噪声淹没,传统的基于短时的能量、过零率、相关以及平均幅度差等检测算法效果都不理想.根据小波变换的特性和语音时域信号的分布特征,提出了一种非高斯噪声背景下语音流检测算法.对含噪语音进行小波分解,观察各层小波系数的统计特征,提取它们的不同特征,从而进行了语音流检测.大量实验表明该算法具有较高的检出率和较低的误检率,可以消除噪声的影响实时处理语音信号.该算法有一定的创新性,在处理非高斯噪音方面很有实用性.

Speech stream detection in non-Gaussian background noise based on statistic characteristics of wavelet coefficient

In practice, non_Gaussian noise is universal and causes serious disturbances, making signal processing difficult. Most strong noises are non_Gaussian, and speech signals are often disturbed and even submerged by strong noises. The traditional algorithm often cannot acquire an ideal effect, which is based on short time energy, short time zero crossing, short time correlation, and short time absolute magnitude difference function. A valid algorithm to detect the speech signal in non_Gaussian background noise was presented according to the statistic characteristics of wavelet coefficient of the speech signal. Speech signal with noise was decomposed by wavelet to investigate the statistic characteristics of wavelet coefficient and different characters were obtained to detect speech signal. Experiments show that the algorithm results in increased correct detection rates and fewer wrong detection rates. The algorithm can steadily resist the influence of noise and can deal with speech signal in real_time. This novel algorithm will be practical when dealing with non_Gaussian noise.