Comunicació presentada a: 13th Sound and Music Computing Conference (SMC 2016), celebrat a Hamburg (Alemanya), del 31 d'agost a 3 de setembre de 2016.

Imitation is the main approach of jingju (also known as Beijing opera) singing training through its inheritance of nearly 200 years. Students learn singing by receiving auditory and gestural feedback cues. The aim of computeraided training is to visually reveal the student’s intonation problem by representing the pitch contour on segmentlevel. In this paper, we propose a technique for this purpose. Pitch contour of each musical note is segmented automatically by a melodic transcription algorithm incorporated with a genre-specific musicological model of jingju singing: bigram note transition probabilities defining the probabilities of a transition from one note to another. A finer segmentation which takes into account the high variability of steady segments in jingju context enables us to analyze the subtle details of the intonation by subdividing the note’s pitch contour into a chain of three basic vocal expression segments: steady, transitory and vibrato. The evaluation suggests that this technique outperforms the state of the art methods for jingju singing. The web prototype implementation of these techniques offers a great potential for both in-class learning and self-learning.