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Optimization of an Automatic Music Genre Classification System via Hyper-Entities

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2 Author(s)
George V. Karkavitsas ; Dept. of Inf., Univ. of Piraeus, Piraeus, Greece ; George A. Tsihrintzis

This paper aims at presenting a âcomputational costâ optimization method in an Automatic Music Genre Classification system. In such systems, the training and validation database is often enormous. Consequently, a system based on a nearest neighbor classifier suffers from high computational cost during the classification process. In such cases, a training instance clustering (per class) can be used. Instances (entities) that belong to the same class and have similar feature values are themselves clustered so as to form a hyper-entity. Thus, any new entity pending classification is compared only to the database hyper-entities and the hyper-entities are responsible for deciding in which class the new entity belongs.

Published in:

Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 2012 Eighth International Conference on

Date of Conference:

18-20 July 2012