Multiclass MTS for saxophone timbre quality inspection using waveform-shape-based features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A study on traditional Malay musical instruments sounds classification system
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Wavelet ridges for musical instrument classification
Journal of Intelligent Information Systems
Computer Methods and Programs in Biomedicine
International Journal of Intelligent Information Technologies
International Journal of Software Science and Computational Intelligence
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In tackling data mining and pattern recognition tasks, finding a compact but effective set of features has often been found to be a crucial step in the overall problem-solving process. In this paper, we present an empirical study on feature analysis for recognition of classical instrument, using machine learning techniques to select and evaluate features extracted from a number of different feature schemes. It is revealed that there is significant redundancy between and within feature schemes commonly used in practice. Our results suggest that further feature analysis research is necessary in order to optimize feature selection and achieve better results for the instrument recognition problem.