Spectral patch based sparse coding for acoustic event detection

In most algorithms for acoustic event detection (AED), frame based acoustic representations are used in acoustic modeling. Due to lack of context information in feature representation, large model confusions may occur during modeling. We have proposed a feature learning and representation algorithm to explore context information from temporal-frequency patches of signal for AED. With the algorithm, a sparse feature was extracted based on an acoustic dictionary composed of a bag of spectral patches. In our previous algorithm, the feature was obtained based on a definition of Euclidian distance between input signal and acoustic dictionary. In this study, we formulate the sparse feature extraction as l₁ regularization in signal reconstruction. The sparsity of the representation is efficiently controlled via varying a regularization parameter. A support vector machine (SVM) classifier was built on the extracted sparse feature for AED. Our experimental results showed that the spectral patch based sparse representation effectively improved the performance by incorporating temporal-frequency context information in modeling.