Subband autocorrelation features for video soundtrack classification
Inspired by the system presented in [1], we have developed novel auditory-model-based features that preserve the fine time structure lost in conventional frame-based features. While the original auditory model is computationally intense, we present a simpler system that runs about ten times faster but achieves equivalent performance. We use these features for video soundtrack classification with the Columbia Consumer Video dataset, showing that the new features alone are roughly comparable to traditional MFCCs, but combining classifiers based on both features achieves a substantial mean Average Precision improvement of 15% over the MFCC baseline.