Environmental Sounds Spectrogram Classification using Log-Gabor Filters and Multiclass Support Vector Machines

This paper presents novel approaches for efficient feature extraction using environmental sound magnitude spectrogram. We propose approach based on the visual domain. This approach included three methods. The first method is based on extraction for each spectrogram a single log-Gabor filter followed by mutual information procedure. In the second method, the spectrogram is passed by the same steps of the first method but with an averaged bank of 12 log-Gabor filter. The third method consists of spectrogram segmentation into three patches, and after that for each spectrogram patch we applied the second method. The classification results prove that the second method is the most efficient in our environmental sound classification system. These methods were tested on a large database containing 10 environmental sound classes. The best performance was obtained by using the multiclass support vector machines (SVM’s), producing an average classification accuracy of 89.62 %.