Environmental Sound Classification Using Log-Gabor Filter

This paper presents novel approaches for efficient feature extraction using environmental sound magnitude spectrogram. We propose approaches based on the visual domain, the spectrogram is passed through a bank of 12 log-Gabor filters, followed by an averaged operation and passed through an optimal feature selection procedure based on mutual information. The proposed methods were tested on a database of 10 sound classes. The evaluation system is realized by using the multiclass support vector machines (SVM's) that gave rise to a recognition rate of the order 89.62 %.