MIRAI: Multi-hierarchical, FS-Tree Based Music Information Retrieval System
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Automatic Singing Voice Recognition Employing Neural Networks and Rough Sets
Transactions on Rough Sets IX
Discriminant feature analysis for music timbre recognition and automatic indexing
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
| Hi-index | 0.00 |
Musical instrument sounds can be classified in various ways, depending on the instrument or articulation classification. This paper presents a number of possible generalizations of musical instruments sounds classification which can be used to construct different hierarchical decision attributes. Each decision attribute will lead us to a new classifier and the same to a different system for automatic indexing of music by instrument sounds and their generalizations. Values of a decision attribute and their generalizations are used to construct atomic queries of a query language built for retrieving musical objects from MIR Database (see http://www.mir.uncc.edu). When query fails, the cooperative strategy will try to find its lowest generalization which does not fail, taking into consideration all available hierarchical attributes. Thus, the music object representing most similar object in the database is returned as the query answer. This paper evaluates only two hierarchical attributes upon the same dataset which contains 2628 distinct musical samples of 102 instruments from McGill University Master Samples (MUMS) CD Collection.