Kernel weighted Fisher sparse analysis on multiple maps for audio event recognition

This work presents a novel approach for audio event recognition. The approach develops a weighted kernel fisher sparse analysis method based on multiple maps. The proposed method consists of maps extraction and kernel weighted Fisher sparse analysis. Two maps are firstly extracted from each audio file, i.e. scale-frequency map and damping-frequency map. The scale and frequency of the Gabor atoms are extracted to construct a scale-frequency map. On the other hand, the damping-frequency map is generated according to the frequency and damping factor of damped atoms. Gabor atoms can be utilized to model human auditory perception, and the damped atoms can be used to model commonly observed damped oscillations in natural signals. This work fuses the advantages of these two dictionaries to improve the performance of the system. During the recognition stage, this work constructs a kernel sparse representation-based classifier via the proposed kernel weighted Fisher sparse analysis to enhance separability. The proposed kernel weighted Fisher sparse analysis combines sparse representation with heteroscedastic kernel weighted discriminant analysis (HKWDA), which is useful for providing a discriminative recognition of audio events because a weighted pairwise Chernoff criterion is utilized in the kernel space. Experiments on a 20-class audio event database indicate that the proposed approach can achieve an accuracy rate of 82.70%. Also, integrating the scale-frequency map with MFCCs increases the accuracy rate to 87.70%.