Abstract
Musical Instrument Identification research is an important problem in Music Information Retrieval (MIR) in which most of the research going on now is using signal processing method. In this paper, at first a model that uses harmonic structured Gaussian mixture for modeling instrument is described and EM algorithm is used to estimate parameters in the model. Therefore features such as Harmonic Temporal Timbre Energy Ratio (HTTER) and Harmonic Temporal Timbre Envelope Similarity (HTTES) are generated from the model. To utilize the features efficiently, a new boosting algorithm based on Probabilistic Decisions is proposed for musical instrument identification. In contrast to the conventional boosting algorithm, which uses a deterministic decision method during the iterations and which does not consider the noise in the data set sufficiently, the new boosting algorithm is proposed to use probabilistic decisions for every hypothesis at the iterations of the boosting scheme, selecting the data events from a dataset, and then combines them. It improves the musical instrument classifier without using boosting approach and the conventional boosting algorithm significantly, which was proved by the experimental.
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Wu, J., Sagayama, S. (2012). Musical Instrument Identification Based on New Boosting Algorithm with Probabilistic Decisions. In: Ystad, S., Aramaki, M., Kronland-Martinet, R., Jensen, K., Mohanty, S. (eds) Speech, Sound and Music Processing: Embracing Research in India. CMMR FRSM 2011 2011. Lecture Notes in Computer Science, vol 7172. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31980-8_6
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DOI: https://doi.org/10.1007/978-3-642-31980-8_6
Publisher Name: Springer, Berlin, Heidelberg
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