Noise Robustness Evaluation of Audio Features in Segment Search

This paper evaluates the noise robustness of audio features in segment search. Active Search is well-known as a fast segment search algorithm, and it has been successfully applied to locate music or video segments (intervals) in huge databases. The noise is generated by MP3 encoding/decoding. The search accuracy is evaluated using F-measure, which is calculated precision and recall. The experiment results show that mel-scaled spectral features are better and have a broader range of search thresholds than linear-scaled features. The low analysis order of the mel-scaled audio features has a search speed that is about 12 times faster, with quite reasonable search accuracy.