Generalized Gaussian Distribution Kullback-Leibler kernel for robust sound event recognition

In previous works, we have developed a spectrogram image feature extraction framework for robust sound event recognition. The basic idea here is to extract useful information from the 2D time-frequency representation of the sound signal to build up specific feature extractions and classifier under noisy conditions. In this paper, we propose a novel robust spectrogram image method where the key is the observed sparsity of the sound spectrogram image in wavelet representations, which is modeled by the Generalized Gaussian Distributions modeling. Furthermore, the Generalized Gaussian Distribution Kullback-Leibler (GGD-KL) kernel SVM is developed to embed the given probabilistic distance into the quadratic programming machine to optimize the classification The experimental result shows the superiority of the proposed method to the previous works and the state-of-the-art in the field.