chetan singh | National Institute of Technology, Warangal (original) (raw)

chetan singh

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Papers by chetan singh

Research paper thumbnail of 11 multi-way hierarchic classification of musical instrument sounds

Musical instrument sounds can be classified in various ways, depending on the instrument or artic... more Musical instrument sounds can be classified in various ways, depending on the instrument or articulation classification. This paper reviews 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 M IR 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. 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.

Research paper thumbnail of 11 multi-way hierarchic classification of musical instrument sounds

Musical instrument sounds can be classified in various ways, depending on the instrument or artic... more Musical instrument sounds can be classified in various ways, depending on the instrument or articulation classification. This paper reviews 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 M IR 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. 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.

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