Abstract
In this work different high-level features and MFCC are taken into account to classify single piano and guitar tones. The features are called high-level because they try to reflect the physical structure of a musical instrument on temporal and spectral levels. Three spectral features and one temporal feature are used for the classification task. The spectral features characterize the distribution of overtones and the temporal feature the energy of a tone. After calculating the features for each tone classification by statistical methods is carried out. Variable selection is used and an interpretation of the selected variables is presented.
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Eichhoff, M., Weihs, C. (2012). Musical Instrument Recognition by High-Level Features. In: Gaul, W., Geyer-Schulz, A., Schmidt-Thieme, L., Kunze, J. (eds) Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24466-7_38
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DOI: https://doi.org/10.1007/978-3-642-24466-7_38
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