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
Pattern Recognition Letters
Feature analysis and classification of classical musical instruments: an empirical study
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Genetic programming for musical sound analysis
EvoMUSART'12 Proceedings of the First international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
Journal of Intelligent Information Systems
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Musical instrument recognition is an important aspect of music information retrieval. In this paper, statistical pattern recognition techniques are utilized to tackle the problem in the context of solo musical phrases. Ten instrument classes from different instrument families are considered. A large sound database is collected from excerpts of musical phrases acquired from commercial recordings translating different instrument instances, performers, and recording conditions. More than 150 signal processing features are studied including new descriptors. Two feature selection techniques, inertia ratio maximization with feature space projection and genetic algorithms are considered in a class pairwise manner whereby the most relevant features are fetched for each instrument pair. For the classification task, experimental results are provided using Gaussian mixture models (GMMs) and support vector machines (SVMs). It is shown that higher recognition rates can be reached with pairwise optimized subsets of features in association with SVM classification using a radial basis function kernel