Journal of Artificial Intelligence Research 22 (2004) 57-91 Submitted 07/03; published 08/04 A Comprehensive Trainable Error Model for Sung Music Queries Colin J. Meek
[email protected] Microsoft Corporation, SQL Server, Building 35/2165, 1 Microsoft Way, Redmond WA 98052 USA William P. Birmingham
[email protected] Grove City College, Math and Computer Science, Faculty Box 2655, 100 Campus Drive, Grove City PA 16127 USA Abstract We propose a model for errors in sung queries, a variant of the hidden Markov model (HMM). This is a solution to the problem of identifying the degree of similarity between a (typically error-laden) sung query and a potential target in a database of musical works, an important problem in the field of music information retrieval. Similarity metrics are a critical component of “query-by-humming” (QBH) applications which search audio and multimedia databases for strong matches to oral queries. Our model comprehensively expresses the types of error or variation between target and query: cumulative and non- cumulative local errors, transposition, tempo and tempo changes, insertions, deletions and modulation. The model is not only expressive, but automatically trainable, or able to learn and generalize from query examples. We present results of simulations, designed to assess the discriminatory potential of the model, and tests with real sung queries, to demonstrate relevance to real-world applications. 1. Introduction Many approaches have been proposed for the identification of viable targets for a query in a music database. Query-by-humming systems attempt to address the needs of the non- expert user, for whom a natural query format – for the purposes of finding a tune, hook or melody of unknown providence – is to sing it.