A multiple feature model for musical similarity retrieval
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Automated musical similarity search and retrieval has gained great attention in recent years, as testified by the number of proposed approaches on this topic. Despite the ``fuzzy'' nature of similarity, which varies from one person to another, perceptual low level features combined with appropriate classification schemes have proven to perform satisfactorily for this task. While a single feature only captures some selective characteristics of an audio signal, this information may, in some cases, not be sufficient to properly identify similarities between songs. This paper presents a system which combines a set of acoustic features for the task of retrieving similar sounding songs in a database with a low computational cost. The methodology for optimum feature selection and combination is explained, as well as the classification process. Finally, the results of a subjective listening test, aimed at assessing the system's performance, are presented and discussed. Some interesting applications of such a system are described.