Abstract
Computer storage and network techniques have brought a tremendous need to find a way to automatically index digital music recordings. In this chapter, state of the art acoustic features for timbre automatic indexing are explored to construct efficient classification models, such as decision tree and KNN. The authors built a database containing more than one million music instrument sound slices, each described by a large number of features including standard MPEG7 audio descriptors, features for speech recognition, and many new audio features developed by the authors, spanning from temporal space to spectral domain. Each classification model was tuned with feature selection based on its distinct characteristics for the blind sound separation system. Based on the experimental results, authors proposed a new framework for MIR with multiple classifiers trained on different features. Inspired by the human recognition experience, timbre estimation based on the hierarchical structure of musical instrument families was investigated. A framework for timbre automatic indexing based on Cascade Classification System was proposed. The authors also discussed the issue of features and classifiers selection during the cascade classification process.
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Jiang, W., Zhang, X., Cohen, A., Raś, Z.W. (2010). Multiple Classifiers for Different Features in Timbre Estimation. In: Ras, Z.W., Tsay, LS. (eds) Advances in Intelligent Information Systems. Studies in Computational Intelligence, vol 265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05183-8_14
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DOI: https://doi.org/10.1007/978-3-642-05183-8_14
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