Gu-Qin as a venerable Chinese plucked-string instrument has its unique performance techniques and enchanting sounds. It is on the UNESCO Representative List of the Intangible Cultural Heritage of Humanity. It is one of the oldest Chinese solo instruments. The variation of Gu-Qin sound is so large that carefullydesigned controls of its computer synthesizer are necessary. We developed a parametric source-filter model for re-synthesizing expressive Gu-Qin notes. It is capable to cover as many as possible combinations of Gu-Qin’s performance techniques. In this paper, a brief discussion of Gu-Qin playing and its special tablature notation are made for understanding the relationship between its performance techniques and its sounds. This work includes a Gu-Qin’s musical notation system and a source-filter model based synthesizer. In addition, we implement an iOS app to demonstrate its low computation complexity and robustness. It is easy to perform improvisation of the sounds because of its friendly user interfaces.

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The control of interpretational properties such as duration, vibrato, and dynamics is important in music performance. Musicians continuously manipulate such properties to achieve different expressive intentions. This paper presents a synthesis system that automatically converts a mechanical, deadpan interpretation to distinct expressions by controlling these expressive factors. Extending from a prior work on expressive musical term analysis, we derive a subset of essential features as the control parameters, such as the relative time position of the energy peak in a note and the mean temporal length of the notes. An algorithm is proposed to manipulate the energy contour (i.e. for dynamics) of a note. The intended expressions of the synthesized sounds are evaluated in terms of the ability of the machine model developed in the prior work. Ten musical expressions such as Risoluto and Maestoso are considered, and the evaluation is done using held-out music pieces. Our evaluations show that it is easier for the machine to recognize the expressions of the synthetic version, comparing to those of the real recordings of an amateur student. While a listening test is under construction as a next step for further performance validation, this work represents to our best knowledge a first attempt to build and quantitatively evaluate a system for EMT analysis/synthesis.

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The same music composition can be performed in different ways, and the differences in performance aspects can strongly change the expression and character of the music. Experienced musicians tend to have their own performance style, which reflects their personality, attitudes and beliefs. In this paper, we present a datadriven analysis of the performance style of two master violinists, Jascha Heifetz and David Fyodorovich Oistrakh to find out their differences. Specifically, from 26 gramophone recordings of each of these two violinists, we compute features characterizing performance aspects including articulation, energy, and vibrato, and then compare their style in terms of the accents and legato groups of the music. Based on our findings, we propose algorithms to synthesize violin audio solo recordings of these two masters from scores, for music compositions that we either have or have not observed in the analysis stage. To our best knowledge, this study represents the first attempt that computationally analyzes and synthesizes the playing style of master violinists.

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