在音樂呈現上，演奏家的演奏方式是一個很重要的因素。舉例來說，演奏者會根據不同的譜面，調整拍速、音量、抖音等。而每位演奏家的個性及想法不盡相同，即使是演奏同一份樂譜也會有各種不同的呈現方法。
在本研究中，我們分析了兩位小提琴大師Jascha Heifetz 和 David Fyodorovich Oistrakh的演奏特徵，並找出兩人在演奏風格上的不同。本研究建立在前人的基礎上，針對兩人都有演奏過的作品，將原有的小提琴協奏曲資料庫擴增，並標記了音符位置、圓滑奏位置、拍速等資訊。我們從資料庫中選出了75個片段，研究演繹方式的要素如:音符連結(音長、緊湊度)、能量以及抖音、圓滑奏速度變化和跳奏等資訊，且特別針對抖音與圓滑奏速度變化進行分析合成，並在能量和其餘的特徵和合成的步驟上，據前人的基礎進行改進。而我們在每一種演繹要素上皆發現了兩位小提琴家的明顯區別。
此外，我們初步歸納出合成的規則，並將找到的特徵套用至小提琴演奏合成上。合成內容包含了原本就在資料庫內的曲目，以及資料庫以外的曲目。

最後，我們也舉辦了一個聽力測試，邀請受測者來為我們的合成成果進行評分，並試圖從測驗結果中得到新的見解。
我們的未來的工作是找出音樂家演奏風格的語法。藉由對小提琴家們的表演進行量化分析，我們得以將抽象的”演奏風格”轉換為一個可以精確描述音樂家的具體規則，甚至可以藉由我們分析的文法來合成出已故名小提琴演奏家的演奏風格的作品供世人欣賞，即使演奏家生前從未演奏過該曲目。

One of the most important factors of an expressive music performance is the interpretation by professional musicians. For example, musicians may adjust the interpretation factors such as tempo, dynamics, vibrato, etc., based on the information of a specific music score. A performer has his/her distinct personality and belief, resulting in different expressions even facing the same music piece.
In this study, we analyze the characteristics of two outstanding violinists, Jascha Heifetz and David Fyodorovich Oistrakh, and find out the differences between their styles. This research is based on several predecessors. To focus on the comparison of their violin-playing styles, we extend our dataset which includes the violin concertos that were performed and recorded by both violinists, and information such as onset, offset, legato, accent, etc. are manually annotated. We study the interpretational factors including articulation (DR and KOT), energy, vibrato, legato speed variation and staccato on 75 excerpts from the dataset. We analyze the features of accents and legato groups and gain insight into the distinct differences between them. In particular, the analysis and synthesis of vibrato and legato speed variation are carried out. Previous-addressed features and the synthesized steps are improved on the basis of the predecessors.
Moreover, based on the above analysis results we develop and apply the synthesis rules in expressive violin synthesis. The synthesis results include the excerpts from the dataset, the excerpts not in the dataset and even excerpts they had never played before.
Finally, we also conducted a listening test that invited the subjects to score our synthetic results and tried to get new insights from the test results.
The future work is to develop a grammar of playing style of a violinist. With the quantitative analysis of these violinists’ performances, we are able to convert the abstract concept of “playing style” to specific rules to describe a musician, and can even synthesize the performances of late musicians based on the grammar, even though he/she had never recorded the piece of music.

Reference
[1] Sandrine Vieillard, Mathieu Roy, and Isabelle Peretz, “Expressiveness in musical motions,” Psychological research, vol. 76, no. 5, pp. 641–653, 2012.
[2] Roberto Bresin, “Articulation rules for automatic music performance,” in Proc. of the International Computer Music Conference (ICMC), 2001.
[3] Esteban Maestre, Rafael Ramírez, Stefan Kersten, and Xavier Serra, “Expressive concatenative synthesis by reusing samples from real performance recordings,” Computer Music Journal, vol. 33, no. 4, pp. 23–42, 2009.
[4] Roberto Bresin and Giovanni Umberto Battel, “Articulation strategies in expressive piano performance analysis of legato, staccato, and repeated notes in performances of the andante movement of Mozart’s sonata in G Major (K 545),” Journal of New Music Research, vol. 29, no. 3, pp. 211–224, 2000.
[5] Akihiro Matsutani, “Study of relationship between performed tones and force of bow holding using silent violin,” Japanese journal of applied physics, vol. 42, no. 6R, pp. 3711, 2003.
[6] Tracy Kwei-Liang Ho, Huann-shyang Lin, Ching-Kong Chen, and Jih-Long Tsai, “Development of a computerbased visualised quantitative learning system for playing violin vibrato,” British Journal of Educational Technology, vol. 46, no. 1, pp. 71–81, 2015.
[7] Andreas P Baader, Oleg Kazennikov, and Mario Wiesendanger, “Coordination of bowing and fingering in violin playing,” Cognitive brain research, vol. 23, no. 2, pp. 436–443, 2005.
[8] Ramon Lopez De Mantaras and Josep Lluis Arcos, “AI and music: From composition to expressive performance,” AI magazine, vol. 23, no. 3, pp. 43, 2002.
[9] Alexis Kirke and Eduardo Reck Miranda, “A survey of computer systems for expressive music performance,” ACM Computing Surveys (CSUR), vol. 42, no. 1, pp. 3, 2009.
[10] Pei-Ching Li, Li Su, Yi-Hsuan Yang, and Alvin WY Su, “Analysis of expressive musical terms in violin using scoreinformed and expression-based audio features.,” in Proc. of the 16th International Society for Music Information Retrieval Conference (ISMIR), 2015, pp. 809–815.
[11] Rafael Ramirez, Esteban Maestre, Alfonso Perez, and Xavier Serra, “Automatic performer identification in celtic violin audio recordings,” Journal of New Music Research, vol. 40, no. 2, pp. 165–174, 2011.
[12] Eitan Ornoy, “Recording analysis of JS Bach’s G Minor Adagio for solo violin (excerpt): A case study,” Journal of Music and Meaning, vol. 6, pp. 2–47, 2008.
[13] Heejung Lee, Violin portamento: An analysis of its use by master violinists in selected nineteenth-century concerti, VDM Publishing, 2009.
[14] Fadi Joseph Bejjani, Lawrence Ferrara, and Lazaros Pavlidis, “A comparative electromyographic and acoustic analysis of violin vibrato in healthy professional violinists,” 1990.
[15] Jae Won Noella Jung, “Jascha Heifetz, David Oistrakh, Joseph Szigeti: Their contributions to the violin repertoire of the twentieth century,” Ph.D. thesis, Florida State University, 2007.

[16] Shih, C. C., Li, P. C., Lin, Y. J., Wang, Y. L., Su, A. W. Y., Su, L., & Yang, Y. H. (2017). “Analysis and synthesis of the violin playing style of Heifetz and Oistrakh.” 466-473. Paper presented at 20th International Conference on Digital Audio Effects, DAFx 2017

[17] Chih-Hong Yang, Pei-Ching Li, Alvin W.Y. Su, Li Su, and Yi-Hsuan Yang, “Automatic violin synthesis using expressive musical term features,” in Proc. of the 16th International Conference on Digital Audio Effects (DAFx), 2016.
[18] Paul Hudak, "The Haskell School of Expression - Learning Functional Programming through Multimedia -," Cambridge University Press New York, ch.20, 1999.
[19] Masataka Goto, Hiroki Hashiguchi, Takuichi Nishimura, and Ryuichi Oka, “RWC music database: Database of copyrightcleared musical pieces and instrument sounds for research purposes,” Transactions of Information Processing Society of Japan(IPSJ), vol. 45, no. 3, pp. 728–738, 2004.