We present a procedure to automatically describe musical articulation gestures used in singing voice performances. We detail a method to characterize temporal evolution of fundamental frequency and energy contours by a set of piece-wise fitting techniques. Based on this, we propose a meaningful parameterization that allows reconstructing contours from a compact set of parameters at different levels. We test the characterization method by applying it to fundamental frequency contours of manually segmented transitions between adjacent notes, and train several classifiers with manually labeled examples. We show the recognition accuracy for different parameterizations and levels of representation.
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