Rough Sets as A Tool for Audio Signal Classification
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Musical instrument recognition using cepstral coefficients and temporal features
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Hierarchical Tree for Dissemination of Polyphonic Noise
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
minedICE: a knowledge discovery platform for neurophysiological artificial intelligence
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Deterministic finite automata in the detection of EEG spikes and seizures
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
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In the continuing goal of codifying the classification of musical sounds and extracting rules for data mining, we present the following methodology of categorization, based on numerical parameters. The motivation for this paper is based upon the fallibility of Hornbostel and Sachs generic classification scheme, used in Music Information Retrieval for instruments. In eliminating the redundancy and discrepancies of Hornbostel and Sachs' classification of musical sounds we present a procedure that draws categorization from numerical attributes, describing both time domain and spectrum of sound. Rather than using classification based directly on Hornbostel and Sachs scheme, we rely on the empirical data describing the log attack, sustainability and harmonicity. We propose a categorization system based upon the empirical musical parameters and then incorporating the resultant structure for classification rules.