Presentations

Background The finger-bowing (also termed as the Detache in French or Yubi-Yumi in Japanese) is a skill used in violin playing to maintain a smooth sound before and after the moment of direction change of a bow. The skill is practiced on a daily basis among violinists as Simon Fischer, a violinist and a pedagogue, described the way of practice in his textbook (Fischer, 2004). Acquiring the skill is fundamental for expert violin performance, yet no kinematic study has been conducted to clarify how a player coordinates the motions of bow and violin body to realize the smooth sound. Aims The aim of this case study was to clarify the fine motion of a violin body and a string in relation to the bow motion during the finger-bowing skill using a high speed camera. Methods A violinist, who graduated from Toho Gakuen School of Music and awarded multiple competitions, played a G3 (196.9 Hz) tone with the finger-bowing skill while maintaining the sound as smooth as possible. The movements during the performance were recorded using a high speed camera (FASTCAM Mini AX, Photron). The shutter speed of the camera was 1/25000 sec and the data were recorded at 10,000 Hz. The motion of the violin body, a string, and a bow were tracked using the DeepLabCut software (Mathis et al., 2018; Nath et al., 2019). The motion of the violin body and bow was smoothed by a 2nd-order Butterworth low-pass filter with a cut-off frequency of 10 Hz to analyze the directional change. The string motion was smoothed by a 2nd-order Butterworth high-pass filter with a cut-off frequency of 20 Hz to analyze the vibration. We first detected the moment at which the direction of a bow was changed. We then analyzed the violin body and string motion before and after the moment of bow change. Results As for the violin body motion, we found that the body changed the direction about 55 msec before the bow changed the direction. As for the string vibration, the amplitude was kept small at around the bow directional change. It was noteworthy that the amplitude of the string vibration increased about 80 msec before the bow changed the direction. Conclusion and Implications Our results indicate that the player accomplishes the smooth violin sound by changing the direction of the violin body as well as increasing the amplitude of string just before the directional change of a bow. As far as we know, this is the first case study that captured the fine motion coordination of bow, violin body, and string with a high speed camera during the finger bowing skill. Our finding may be useful for developing objective training methods for violin learners.

Inappropriate music copyright lawsuits inhibit music creativity and waste millions of taxpayer dollars annually, but there are few objective guidelines for applying copyright law in claims involving musical works. Recent research has proposed objective algorithms to automatically calculate musical similarity, but there remains almost no relevant perceptual data. Aims Our study aims at supporting the legal system to make decisions on music copyright lawsuits more efficiently and accurately. Current proposed automated algorithms for music similarity reduce subjectivity in music copyright decisions but have not been tested against perceptual data. Thus, we collected both perceptual and automated data to help determine objective standards for how much copying is required to be considered as substantial similarity. Methods In previous study (Yuan et al., 2020), we chose 17 adjudicated copyright cases whose main copyright issue focused on substantial similarity of melodies. To improve it, we expanded our database to include a larger and more diverse sample of 46 cases from the Music Copyright Infringement Resource (Cronin, 2020), including cases focused on similarity in timbre, lyrics, etc. as well as melodic similarity. We will conduct a perceptual experiment to collect 20 participants' perceptual judgements on music similarity of the 46 copyright cases and compare the results with the similarity levels calculated by 2 automated algorithms which were designed to calculate melodic and audio similarity, respectively. We will apply Percent Melodic Identity method which was developed to quantify melodic evolution based on automatic sequence alignment algorithms to measure melodic similarity (Savage et al., 2015). We plan to test CQTNet model, which used Convolutional Neural Network architecture for audio cover song identification (Yu et al., 2020), for its ability to evaluate audio similarity and music copyright infringement. Results Our previous application found the 20 participants’ judgement of infringement matched the official decisions in between 49-58% of the 17 selected cases in all three conditions (given full-audio, melody-only, or lyrics-only versions). Both automated algorithms designed for melodic and audio similarity respectively were able to match past decisions with identical accuracy of 71% (12/17 cases). The updated study with larger sample of 46 cases which were selected with strictly-designed criteria would probably reduce the failures in automatic analysis caused by non-musical aspects. We also expect the newly-introduced CQTNet model could show better performance in music similarity. References Cronin, C. (2020). Music Copyright Infringement Resource, http://mcir.usc.edu. Savage, P. E., & Atkinson, Q. D. (2015). Automatic tune family identification by musical sequence alignment. ISMIR. Yu, Z., Xu, X., Chen, X., & Yang, D. (2020). Learning a Representation for Cover Song Identification Using Convolutional Neural Network. ICASSP. Yuan, Y., Oishi, S., Cronin, C., Müllensiefen, D., Atkinson, Q. D., Fujii, S., & Savage, P. E. (2020). Perceptual vs. automated judgements of music copyright infringement. ISMIR.

【目的】精神科入院中の統合失調症患者におけるリズム感を客観的に評価し、リズム感と MMNの関係性について検討する。【方法】当院入院中の統合失調症患者で同意が得られた対象者13名に対し、 iOS版 The Harvard Beat Assessment Test(以下、 H-BAT)を実施しリズム感を計測した。 H-BAT実施日の±1週間以内を目安に MMNを 計測し、相関係数を算出した。 MMNについては加算平均波形を用いて、刺激開始後140ms-250msの間で頂点振 幅・潜時を算出した。【結果】統合失調症患者リズム感の評価では、 H-BATの BITperが5.39±4.96msecであった。 MMNは Fz潜時が213.69±24.09msec、振幅が-2.95±1.29μ V、 Cz潜時が213.08±28.80msec、振幅 が-2.77±1.45μ Vであった。 H-BATの下位項目である BITper(音の間隔の変化を聴き分ける能力)と、 MMNの 振幅には弱い正の相関を認めたが、有意差は認められなかった。現在詳細な解析を行っている。発表時に、考察を含めて解析結果を報告する。

アメリカボストンで開催さいれた世界バイオメカニクス会議にて、プロドラマーが音量調節を行う際の上肢運動の三次元運動学解析の結果について報告した。

ドラマーとして音楽を演奏する傍ら、音楽家の身体運動の不思議さに魅了され、音楽家の運動スキル研究をこれまで行ってきた（Fujii et al., J Neurophysiol, 2010 他）。研究すればするほど、ヒトにとって音楽とは何か、リズムとは何か、より本質的な問いに迫りたいと思うようになり、近年では音楽リズム同期・同調の発達的起源や（Fujii et al., PLoS ONE, 2014）、音楽リズムの知覚生成能力の個人差（Fujii & Schlaug, Front Hum Neurosci, 2013）、言語・音楽リズムの脳内処理（Fujii & Wan, Front Hum Neurosci, 2014）、精神疾患とリズム知覚生成能力の関連性（Fujii et al., ICMPC, 2018）等に焦点を当て研究を行っている。本シンポジウムでは特に、生後３～４か月の乳児の手足の運動リズムと音楽リズムの同調・同期現象、音楽リズム知覚生成の個人差評価のために開発したハーバードビート評価テスト、音楽リズムの知覚生成能力の個人差と脳の灰白質構造の関連、言語・音楽リズムの脳内処理の共通性と相違性、統合失調症患者のリズム知覚生成能力等について話題提供させていただく予定である。音楽リズムの知覚生成をキーワードとして、ヒトの音楽性の起源について、皆さまと闊達に議論できれば幸いである。