Environmental Sounds Classification Based on Visual Features

Abstract

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.

Environmental sound recognition: A survey

“…The original application in [43] was texture classification, yet the plausible use for music instrument classification was mentioned. Souli and Lachiri subsequently used this method for ESR in [26]. They also proposed another set of nonlinear features in [27].…”

“…This method extracted features from the log-Gabor filtered spectrogram (instead of the raw spectrogram). Since no performance comparison was made between features obtained from the log-Gabor filtered spectrogram and the raw spectrogram in [26], the advantages and shortcomings of this approach need to be explored furthermore.…”

Environmental sound classification using log-Gabor filter

“…This approach consists in computation of 12 log-Gabor filters from the environmental sounds spectrograms, with 2 different scales (1,2) and 6 different orientations (1,2,3,4,5,6), this extraction allows the best correlate of signal structures. Then, for each single filter result we calculated the magnitude, after that, we passed through on mutual information (MI) algorithm to find an optimal feature vector that can next be passed for classification phase [9].…”

“…To improve our work results realized in [5], the idea consists in elaborating other methods based also on time-frequency representation, but with new approaches.…”

“…Recently, little efforts emerge in the visual domain [4], [5].These studies, based on the visual signature extracted from the sound's spectrogram, and demonstrate that the time-frequency representation techniques can be applied to musical sounds, or environmental sounds and can produce a good result for classification. To improve our work results realized in [5], the idea consists in elaborating other methods based also on time-frequency representation, but with new approaches.…”