Acoustic feature extraction by tensor-based sparse representation for sound effects classification

This paper describes a method to extract time-frequency (TF) audio features by tensor-based sparse approximation for sound effects classification. In the proposed method, the observed data is encoded as a higher-order tensor and discriminative features are extracted in spectrotemporal domain. Firstly, audio signals are represented by a joint time-frequency-duration tensor based on sparse approximation; then tensor factorization is applied to calculate feature vectors. The three arrays of the proposed tensor are used to represent frequency, time and duration of transient TF atoms respectively. Experimental results show that exploiting tensor representation allows to characterize distinctive transient TF atoms, yielding an average accuracy improvement of 9.7% and 12.5% compared with matching pursuit (MP) and MFCC features.