关键词: 生态声音辨识, 匹配追踪, 萤火虫算法, 信号稀疏分解, Mel频率倒谱系数

Abstract: The paper proposes a robust ecological environmental sounds identification system by using optimized matching pursuit algorithm which is optimized by Glowworm Swarm Optimization（GSO） to improve the performance of sound recognition in real environmental noisy conditions. It uses the Matching Pursuit（MP） to decompose the sound signal sparsely, and reconstructs its inner structure to reduce the influence of the noise. GSO is employed to speed up the searching for the best atom in each process of decomposition. Different feature sets are extracted. As the performance of popular Mel-Frequency Cepstral Coefficients（MFCC） degrades due to sensitivity to noise, MP based time-frequency features and Pitch are adopted to supplant the MFCCs feature. Through the SVM classifier, 56 subclasses of 4 classes of ecological environmental sounds are tested for the comparison experiments in different environments under different SNRs. The experimental results show that this approach outperforms traditional methods of MFCCs and SVM, as the average identification accuracy and robustness for ecological environmental sounds are improved to a different degree, especially under the conditions of SNRs lower than 30 dB.

Key words: ecological environmental sounds recognition, matching pursuit, glowworm swarm optimization, sparse decomposition, mel-frequency cepstral coefficients