Masaru Fujieda, Takahiro Murakami, and Yoshihisa Ishida
Multi-pitch estimation, automatic music transcription, support vector machine
In this paper, an approach to multi-pitch estimation using a support vector machine (SVM) is proposed. For automatic music transcription, an SVM is used often for identifying the sounds of musical instruments but rarely for estimation of the pitch – the fundamental frequency. Our approach is based on the SVM characteristic of finding the ideal hypersurface that separates the input vectors into two classes. The proposed system achieves multi-pitch estimation by effectively using the margins between the hypersurfaces and the input vectors. In addition, our system does not require chord sounds for the SVM training instances. Experiments using sound data generated by artificially compounding real audio signals show that the proposed method drastically reduces the number of training instances.
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