A query-by-example music retrieval system using feature and decision fusion

Abstract

An attractive topic of Music Information Retrieval (MIR) is focused on query-by-example (QBE), which receives a user-provided query and aims to find the target song from an associated music dataset. In this paper, we use feature and decision fusion techniques to develop a two-stage accurate and rapid QBE based MIR system. For this purpose, a proposed diverse ensemble of recognizers automatically recognizes the genre of the query in first stage. This diversity is yielded through feature extraction over different frequency bands followed by feature fusion to train the recognizers, and then a decision fusion technique fuses the individual results obtained by members of ensemble. Second stage measures similarity between query and other contents of dataset having the same genre with the query to find the target song. To accomplish this, a distance measure that here is Kullback–Leibler divergence is utilized. In this stage, a genre-adaptive feature extraction method is proposed, and features are also fused by a feature fusion technique. The effectiveness of the feature and decision fusion techniques in our two-stage system (genre recognition; song retrieval) is evaluated experimentally that shows a significant improvement in terms of accuracy and retrieval time in comparison with a system for which those techniques are not applied.