Abstract
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.
Antonio Gonzalez-Pardo
Associate Professor
Lecturer at the Computer Science Department. Main research interests are related to Computational Intelligence and Metaheuristics applied to Social Networks Analysis, and the optimization of graph-based problems.