Abstract

Language Processing. Do not distribute! We propose an automatic method for measuring music similarity using audio features so we can enhance the current generation of taxonomy-based music search engines and recommender systems. Efficiency is important in an Internet-connected world, where users have access to millions of tracks. Brute-force algorithms for searching through this content are not practical. Many previous approaches to track similarity require pair-wise processing between all audio features in a database and therefore are generally not practical for large collections. Our features are time-ordered overlapping fixed-length subsequences of equal-temperament pitch-class profiles and log-frequency cepstral coefficients; the technique is analogous to the technique of shingling used for text retrieval. We use locality sensitive hashing to implement approximate matching for our high-dimensional audio shingles. This approach retrieves near neighbors within a specified distance of the query rather than retrieving only the nearest neighbors; the degree of approximation, ɛ, is a parameter. LSH achieves sub linear query time performance with respect to the number of tracks in a collection but requires an accurate threshold on retrieval distance for efficient performance. In this paper, we present a new method for estimating the optimal search radius for LSH retrieval tasks by modeling the between-shingle distance distributions for non-similar audio shingles. We derive an estimator for a minimum distance for two shingles to be considered drawn from different tracks. therefore, are considered to be drawn from similar tracks. We evaluate our proposed methods on three contrasting music similarity tasks: retrieval of mis-attributed recordings (Apocrypha), retrieval of the same work by performed by different artists (Opus) and retrieval of edited and sampled versions of a query track by remix artists (Remixes). Our results achieve near-perfect performance in the first two tasks and 80 % precision at 70 % recall in the third task.

Citations