Informed Selection of Frames for Music Similarity Computation.

Description

In this paper we present a new method to compute frame based audio 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 particular, 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 propose 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.

Period	02 Sept 2009
Event title	12th International Conference on Digital Audio Effects (DAFx-09)
Event type	Conference
Location	ItalyShow on map