Kernel discriminant analysis for environmental sound recognition based on acoustic subspace

In this paper, we propose an effective discriminant subspace learning framework to recognize the environmental sounds. Firstly, Gabor transform is adopted to characterize the time-frequency distributions of environmental sounds. We further encode the prominent time-frequency patterns with low rank representation by extracting the subspace from Gabor spectrogram. Unlike conventional sound recognition schemes that are mostly based on acoustic feature vectors, we treat the acoustic subspaces (matrixes) as basic elements for recognition, retaining rich temporal-spectral contextual information. At recognition stage, we employ kernel Fisher discriminant analysis to effectively exploit the class conditional distributions of environmental sounds which are favorable for performing multi-class classification. With a well developed kernel function, the proposed approach achieved superior recognition performance on RWCP sound scene database, compared with the existing methods.