Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms
Applied Intelligence
Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on soft computing for information mining
Feature Selection in Automatic Music Genre Classification
ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
Improving fuzzy logic controllers obtained by experts: a case study in HVAC systems
Applied Intelligence
IEEE Transactions on Fuzzy Systems
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Automatic music retrieval processes rely on classification and tagging systems. Among the tags usually employed for classifying music, genre is a prominent one. This paper presents an ensemble of classifiers that uses a hybrid genetic fuzzy approach. By using a set of Fuzzy Rule Based Systems automatically tuned by means of a Genetic Algorithm, and structured in two layers, the system is capable of correctly classifying classical and jazz samples randomly chosen from a wide set of authors and styles. The ensemble is built on top of a previously developed method that profits from non-precise information by using Fuzzy Systems. The inherently ambiguous information frequently related to music genre is properly managed by a Fuzzy Rule Based System that focuses on random samples extracted from the audio to be analyzed. A set of these Fuzzy Rule Based Systems are then applied simultaneously to a number of samples, and the final system is in charge of processing the partial information obtained by each of the Fuzzy Rule Based System. The experimental setup and results take into account harmonic principles and their relationship with the specific genre considered. The system is capable of providing good classification accuracy by using an extremely narrow set of features.