Ecological environmental sounds classification based on genetic algorithm and matching pursuit sparse decomposition

The Mel-frequency cepstral coefficients (MFCCs) based on human auditory characteristics are widely used for audio recognition. However, the performance of MFCC-based audio recognition degrades due to noise interference. In consideration of this, we propose the matching pursuit (MP) sparse representation algorithm based on genetic algorithm (GA) improved by elite strategy and evolution reversal to accomplish the task of filtering out extraneous noise. In the first step, MP is carried out to represent the ecological environmental signal´s inner structure. The second step consists of MFCCs feature extraction. Finally, two different classifiers, Support Vector Machine (SVM) and Gaussian mixture model (GMM) were performed and compared using the proposed features. Experimental results showed that the SVM-based classifier outperforms the GMM classifier and indicated that this method with sparse representation achieved improved performance in noisy environments.