摘要
城市环境中包含着各种各样的杂音,针对这种复杂的声音识别环境,该文提出一种基于MFCC与CNN的声音识别方法.首先对城市环境声音样本进行梅尔特征提取,取得特征图之后由卷积神经网络进行训练、测试获得CNN特征,最后由SVM分类器识别分类,并将其与常见的音频识别方法对比分析,在识别速度和识别率上均有所优化,实验表明,此方法在复杂环境下能够得到较好的声音识别效果.
There are all kinds of noises in the urban environment.In view of this complex voice recognition environment,this paper proposes a voice recognition method based on MFCC and CNN.At first,meier feature extraction is carried out on urban environmental sound samples,and then the feature map is obtained,and then the CNN feature is trained and tested by convolutional neural network.Finally,the classification is recognized by SVM classifier.Compared with the common audio recognition methods,the method is optimized in recognition speed and recognition rate,which proves that the method can obtain better sound recognition effect in complex environment.
引文
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