Analysis of Minimum Distances in High-Dimensional Musical Spaces

Abstract--We propose an automatic method for measuring content-based music similarity, enhancing the current generation of music search engines and recommender systems. Many previous approaches to track similarity require brute-force, pair-wise processing between all audio features in a database and therefore are not practical for large collections. However, in an Internet-connected world, where users have access to millions of musical tracks, efficiency is crucial. Our approach uses features extracted from unlabeled audio data and near-neigbor retrieval using a distance threshold, determined by analysis, to solve a range of retrieval tasks. The tasks require temporal features--analogous to the technique of shingling used for text retrieval. To measure similarity, we count pairs of audio shingles, between a query and target track, that are below a distance threshold. The distribution of between-shingle distances is different for each database; therefore, we present an analysis of the d...

• Type: Journal
• Year: 2008
• Where: TASLP
• Authors: Michael Casey, Christophe Rhodes, Malcolm Slaney
• Keywords: Content-based Music Similarity, Distance Threshold, Music Similarity, TASLP 2008