Minimal test collections for low-cost evaluation of Audio Music Similarity and Retrieval systems

Abstract

Reliable evaluation of Information Retrieval systems requires large amounts of relevance judgments. Making these annotations is not only tedious but also complex for many Music Information Retrieval tasks. As a result, performing such evaluations usually requires too much effort. A low-cost alternative is the application of Minimal Test Collections algorithms, which offer very reliable results while significantly reducing the required annotation effort. The idea is to represent effectiveness scores as random variables that can be estimated, iteratively selecting which documents to judge so that we can compute accurate estimates with a certain degree of confidence and with the least effort. In this paper we show the application of Minimal Test Collections to the evaluation of the Audio Music Similarity and Retrieval task, run by the annual MIREX evaluation campaign. An analysis with the MIREX 2007, 2009, 2010 and 2011 data shows that with as little as 2 % of the total judgments we can obtain accurate estimates of the ranking of systems. We also present a method to rank systems without making any annotations, which can be successfully used when little or no resources are available.