Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
Representations of musical signals
Music, signals, and representations: a survey
Representations of musical signals
Qualitative aspects of signal processing through dynamic neural networks
Representations of musical signals
Introduction to artificial neural systems
Introduction to artificial neural systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Principles of multimedia database systems
Principles of multimedia database systems
Digital Signal Processing: A Practical Approach
Digital Signal Processing: A Practical Approach
Rough Sets as A Tool for Audio Signal Classification
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Towards Musical Data Classification via Wavelet Analysis
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Optimizing Self-Organizing Timbre Maps: Two Approaches
Music, Gestalt, and Computing - Studies in Cognitive and Systematic Musicology
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Contents-based searching through audio data is basically restricted to metadata, which are attached manually to the file. Otherwise, users have to look for the specific musical information alone. Nevertheles, when classifiers based on descriptors extracted from sounds analytically are used, automatic classification can be in some cases possible. For instance, wavelet analysis can be used as a basis for automatic classification of audio data. In this paper, classification of musical instrument sounds based on wavelet parameterization is described. Decision trees and rough set based algorithms are used as classification tools. The parameterization is very simple, but the efficiency of classification proves that automatic classification of these sounds is possible.