INFORMED SELECTION OF FRAMES FOR MUSIC SIMILARITY COMPUTATION

In this paper we present a new method to compute frame based au- dio similarities, based on nearest neighbour density estimation. We do not recommend it is as a practical method for large collections because of the high runtime. Rather, we use this new method for a detailed analysis to get a deeper insight on how a bag of frames approach (BOF) determines similarities among songs, and in par- ticular, to identify those audio frames that make two songs similar from a machine's point of view. Our analysis reveals that audio frames of very low energy, which are of course not the most salient with respect to human perception, have a surprisingly big influence on current similarity measures. Based on this observation we pro- pose to remove these low-energy frames before computing song models and show, via classification experiments, that the proposed frame selection strategy improves the audio similarity measure.