计算机工程与应用Issue(2)：198-204,7.DOI:10.3778/j.issn.1002-8331.1303-0285
基于萤火虫算法的匹配追踪用于生态声音辨识
Glowworm swarm optimization and matching pursuit sparse decomposition for ecological environmental sounds identification
摘要
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 envi-ronmental sounds are tested for the comparison experiments in different environments under different SNRs. The experi-mental 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 con-ditions of SNRs lower than 30 dB.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Key words
ecological environmental sounds recognition/matching pursuit/glowworm swarm optimization/sparse decom-position/mel-frequency cepstral coefficients分类
信息技术与安全科学引用本文复制引用
欧阳桢,李应..基于萤火虫算法的匹配追踪用于生态声音辨识[J].计算机工程与应用,2015,(2):198-204,7.基金项目
国家自然科学基金（No.61075022）。 （）
