Abstract
In presenting the appropriate data sets, various data representation and feature extraction methods have been discovered previously. However, almost all the existing methods are utilized based on the Western musical instruments. In this study, the data representation and feature extraction methods are applied towards Traditional Malay musical instruments sounds classification. The impact of five factors that might affecting the classification accuracy which are the audio length, segmented frame size, starting point, data distribution and data fraction (for training and testing) are investigated. The perception-based and MFCC features schemes with total of 37 features was used. While, Multi-Layered Perceptrons classifier is employed to evaluate the modified data sets in terms of the classification performance. The results show that the highest accuracy of 97.37% was obtained from the best data sets with the combination of full features.
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Senan, N., Ibrahim, R., Nawi, N.M., Mokji, M.M., Herawan, T. (2010). The Ideal Data Representation for Feature Extraction of Traditional Malay Musical Instrument Sounds Classification. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_43
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DOI: https://doi.org/10.1007/978-3-642-14922-1_43
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