CAJ | 학술논문

针对生态环境中背景噪声对声音辨识产生干扰的问题，提出利用萤火虫算法优化匹配追踪的方法进行生态声音辨识。利用匹配追踪（MP）稀疏分解声音信号，在保留信号主体结构的前提下对其进行重构，减小噪声的影响。使用萤火虫（GSO）算法优化搜索最佳匹配原子，实现MP快速分解。对重构信号提取Mel频率倒谱系数（MFCCs）， MP时频特征及基音频率。结合支持向量机（SVM）对56种生态声音在不同环境和信噪比情况下进行分类识别。实验结果表明，与传统MFCC与SVM的方法相比，该方法对生态声音在不同信噪比下的识别性能得到不同程度的改善并且具有较好的抗噪性，尤其适合低信噪比（30 dB以下）噪声情境下使用。
침대생태배경중배경조성대성음변식산생간우적문제，제출이용형화충산법우화필배추종적방법진행생태성음변식。이용필배추종（MP）희소분해성음신호，재보류신호주체결구적전제하대기진행중구，감소조성적영향。사용형화충（GSO）산법우화수색최가필배원자，실현MP쾌속분해。대중구신호제취Mel빈솔도보계수（MFCCs）， MP시빈특정급기음빈솔。결합지지향량궤（SVM）대56충생태성음재불동배경화신조비정황하진행분류식별。실험결과표명，여전통MFCC여SVM적방법상비，해방법대생태성음재불동신조비하적식별성능득도불동정도적개선병차구유교호적항조성，우기괄합저신조비（30 dB이하）조성정경하사용。
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.