关键词: 能量检测, 小波包分解子带倒谱系数, 梅尔频率倒谱系数, 支持向量机, 鸟鸣识别

Abstract: For the purpose of improving the recognition accuracy of bird sounds in various kinds of noisy environments in real world, a new bird sounds recognition approach based on energy detection was proposed. First of all, the useful bird sound signals were detected and selected by the method of energy detection from the bird sounds with noises. Secondly, according to the distribution of Mel scale, the feature of Wavelet Packet decomposition Subband Cepstral Coefficient (WPSCC) was extracted from the useful signals. Finally, the classifier of Support Vector Machine (SVM) was applied to model on the WPSCC and Mel-Frequency Cepstral Coefficient (MFCC) respectively for classification and identification. Meanwhile, the comparisons of recognition performance difference were implemented on 15 kinds of bird sounds at different Signal-to-Noise Ratio (SNR) in different noises, before or after energy detection. The experimental results show that WPSCC has better noise immunity function, and the recognition performance after energy detection can be greatly improved, which means it is more suitable for the bird sounds recognition in complex environments.

Key words: energy detection, Wavelet Packet decomposition Subband Cepstral Coefficient (WPSCC), Mel-Frequency Cepstral Coefficient (MFCC), Support Vector Machine (SVM), bird sounds recognition