关键词: Mel频率倒谱系数, 高斯混合模型, 自然环境声音的识别, 投票裁决

Abstract: A recognition method for natural sounds based on Gaussian Mixture Model（GMM） distribution is proposed.Mel-Frequency Cepstral Coefficients（MFCCs） are used to analyze natural sounds for their feature extraction.The expectation maximization algorithm is used to learn a Gaussian mixture model distribution of MFCCs for the set of audio feature vectors that describe each sound.Minimum classification error criterion and vote rule are used to yield higher recognition accuracy for natural sounds.Experimentally，compared with K-Nearest Neighbor（KNN） method，GMM is able to achieve a higher accuracy rate for discriminating 36 classes of natural sounds.The classified accuracy rate of GMM reaches to 95.83%.

Key words: Mel-frequency cepstral coefficients, Gaussian mixture model, natural sounds recognition, vote rule