MFCC combined with sparse coding for sound event classification under different noise environments

In recent years, the most popular method for sound event classification can be classified into two types: 1) Extract MFCC or PLP, then train classifier for classification; 2) Convert sound into spectrogram, then use the method of image classification. However, the two methods have not achieved satisfied performance. In order to promote the classification performance, we present classification method for a sound event based on MFCC and sparse coding which has a good effect on capturing the high-level representation features of the input data. Then the coefficients of sparse coding will be employed as new sound event features to train the classification model. Our experimental results demonstrate the great robustness, adaptability and an obvious improvement on sound event classification.

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