This paper presents a method aimed at classification of the environmental sounds in the visual domain by using the scale and translation invariance. We present a new approach that extracts visual features from sound spectrograms. We suggest to apply support vector machines (SVM's) in order to address sound classification. Indeed, in the proposed method we explore sound spectrograms as texture images, and extracts the time-frequency structures by using a translation-invariant wavelet transform and a patch transform alternated with local maximum and global maximum to pursuit scale and translation invariance. We illustrate the performance of this method on an audio database, which composed of 10 sounds classes. The obtained recognition rate is of the order 91.82 % with the multiclass decomposition method: One-Against-One.
In this paper, we propose a robust environmental sound spectrogram classification approach; its purpose is surveillance and security applications based on the reassignment method and log-Gabor filters. Besides, the reassignment method is applied to the spectrogram to improve the readability of the time-frequency representation, and to assure a better localization of the signal components. In this approach the reassigned spectrogram is passed through a bank of 12 log-Gabor filter concatenation applied to three spectrogram patches, and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criterion. The proposed method is tested on a large database consists of 1000 environmental sounds belonging to ten classes. The averaged recognition accuracy is of order 90.87% which obtained using the multiclass support vector machines (SVM's).
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