Influence of music representation on compression-based clustering


Multimedia Information Retrieval is currently a hot research topic due the popularity of the World Wide Web and the huge amount of multimedia data available. There exists an increasing interest to design and develop new methods and techniques to represent and classify this kind of information. Among the different sources of multimedia information currently available, we have decided to work with music audio files. Three different music representations (binary code, wave information, and SAX) have been used to study how the selection of a particular representation could affect a clustering process based on a set of similarity clusters. Two different algorithms (a hierarchical clustering method based on the quartet tree method and a genetic algorithm) have been applied to automatically perform the clustering. A compression distance, the Normalized Compression Distance (NCD), has been used to generate the similarities among the music files. This distance is parameter-free and widely applicable so we can use it directly with different formats and representations. The paper shows some experimental results using these representations and compares the behavior of both clustering methods.

IEEE Congress on Evolutionary Computation
Antonio Gonzalez-Pardo
Antonio Gonzalez-Pardo
Profesor Titular de Universidad

Profesor del Departamento de Informática. Sus principales intereses de investigación están relacionados con la inteligencia computacional y la metaheurística aplicada al análisis de redes sociales, y la optimización de problemas basados en grafos.