Abstract: Feature extraction is the foundation of the audio classification，and good features will enhance the classification accuracy effectively.In this paper，Mel-frequency cepstrum coefficients are extracted from frequency domain of audio.At the same time，features are extracted from wavelet domain after discrete wavelet transform is done for each frame of the audio.Then the features from the frequency domain and wavelet domain are combined to calculate the statistical features.Finally，audio template is established according to the Support Vector Machine（SVM），and it is classified and identified into speech，music and speech with music.Tests show that the method gets comparatively high identification accuracy.

摘要： 音频特征提取是音频分类的基础，好的特征将会有效提高分类精度。在提取频域特征Mel频率倒谱系数（MFCC）的同时，对每一帧信号做离散小波变换，提取小波域特征，把频域和小波域特征相结合计算其统计特征。通过SVM模型建立音频模板，对纯语音、音乐及带背景音乐的语音进行分类识别，取得了较高的识别精度。