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Comparison of features in musical instrument identification using artificial neural networks
Date
2008
Abstract
This paper examines the use of a number of auditory features in identifying musical instruments. The Temporal Envelope, Centroid, Melfrequency Cepstral Coefficients (MFCCs), Inharmonicity, Spectral Irregularity and Number of Spectral Peaks are all examined. By using these features to train a Multi-Layered Perceptron (MLP), it is determined that the MFCCs are the most efficient of these features in musical instrument identification. The Inharmonicity, Spectral Irregularity and Number of Spectral Peaks offered no benefit to the classifier. Of the instruments studied, the piano was most accurately classified and the violin was the least accurately classified instrument.
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Description
Non-peer-reviewed
Publisher
Springer
Citation
5th International Symposium on Computer Music Modeling and Retrieval pp. 19-33
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Funding Information
Science Foundation Ireland (SFI)