Sparse representation based on a bag of spectral exemplars for acoustic event detection

Acoustic event detection is an important step for audio content analysis and retrieval. Traditional detection techniques model the acoustic events on frame-based spectral features. Considering the temporal-frequency structures of acoustic events may be distributed in time-scales beyond frames, we propose to represent those structures as a bag of spectral patch exemplars. In order to learn the representative exemplars, k-means clustering based vector quantization (VQ) was applied on the whitened spectral patches which makes the learned exemplars focus on high-order statistical structure. With the learned spectral exemplars, a sparse feature representation is extracted based on the similarity measurement to the learned exemplars. A support vector machine (SVM) classifier was built on the sparse representation for acoustic event detection. Our experimental results showed that the sparse representation based on the patch based exemplars significantly improved the performance compared with traditional frame based representations.