基于小波矩和BP网络的声音识别

摘要

目前大多数声音识别系统在无噪声环境下可以达到很高的识别率，但是在噪声环境下，识别率急剧下降。针对这个问题，提出一种基于小波矩和BP网络的声音识别方法。根据声音信号生成声谱图；通过小波矩对声谱图进行特征提取，选取有代表性意义的特征参数；根据选取的参数进行BP网络分类识别，从而识别声音的种类。实验结果表明，该方法在不同噪声种类以及不同信噪比的噪声环境下仍然具有较好的识别效果，克服了低信噪比下识别率低的缺陷。%Currently, most of voice recognition systems can reach a very high recognition rate in noiseless conditions. However, the sound recognition sharply declines in noisy environment. Given this problem, this paper puts forward a voice recognition method based on wavelet moment and BP network. The system generates sonograms according to sound signals. Then, it uses wavelet moment to extract features from spectrogram, and select the representative characteristic parameters. It uses BP neutral network to classify and recognize sounds based on characteristic parameters. The experimental results show that the method can achieve good results in noise environment with different types of noise or different SNR, which overcomes the defect of the low recognition rate under low SNR.