研究発表 - 藤井　進也

Pitch perception and production ability in patients with schizophrenia

Tobari Y., Ogyu K., Noda Y., Honda S., Matsushita K.,Koizumi T., Koreki A., Mimura M., Nakajima S., & Fujii S.

[国際会議]  The 2022 Meeting of the Society for Music Perception and Cognition (SMPC 2022) （Portland, OR） , 

2022年08月

, 

口頭発表（一般）, Society for Music Perception and Cognition

Sight vs. sound in the judgment of music performance: Cross-cultural evidence from classical piano and Tsugaru shamisen competitions

Chiba G., Ozaki Y., Fujii S., & Savage P. E.

[国際会議]  The 2022 Meeting of the Society for Music Perception and Cognition (SMPC 2022) （Portland, OR） , 

2022年08月

, 

口頭発表（一般）, Society for Music Perception and Cognition

Synchronization Error and Sound Amplitude during Drum-kit Playing with A Metronome in A Professional Drummer with Musician’s Dystonia

Honda K., Sata S., Komine K., Yamaguchi S., Kashino M., & Fujii S.

[国際会議]  The 2022 Meeting of the Society for Music Perception and Cognition (SMPC 2022) （Portland, OR） , 

2022年08月

, 

口頭発表（一般）, Society for Music Perception and Cognition

Foot Tapping Speed of Drummers, Non-drummer Musicians, and Non-musicians

Sata S., Honda K., & Fujii S.

[国際会議]  The 2022 Meeting of the Society for Music Perception and Cognition (SMPC 2022) （Portland, OR） , 

2022年08月

, 

口頭発表（一般）, Society for Music Perception and Cognition

Human and automated judgements of musical similarity in a global sample

Daikoku H., Ding S., Benetos E., Wood A. L. C., Shimozono T., Fujii S. & Savage P. E.

The seventh international conference on Analytical Approaches to World Music (AAWM 7) and the tenth International Workshop on Folk Music Analysis (FMA 2022), 

2022年06月

Visual attention for a face-like stimulus and correlates with autistic traits: using a henohenomoheji-type stimulus

Sugiyama M., Yamanishi S., Fujii S., Mori M

[国際会議]  The 22nd Annual Meeting of the Vision Sciences Society (VSS2022), 

2022年05月

, 

ポスター発表

リズム・音楽・身体がつなぐ音楽科学と舞踊科学の未来

藤井進也

[国内会議]  第一回日本ダンス研究会, 

2022年04月

, 

口頭発表（招待・特別）

ドラムパターン演奏中のメトロノーム同期誤差 - 右下肢⾳楽家ジストニアの症例 -

本田一暁, 佐田静香, 小嶺美月, 山口智史, 柏野牧夫, 藤井進也

[国内会議]  生理研研究会「スポーツからみる日常と非日常の神経生理学」 （オンライン） , 

2022年03月

, 

ポスター発表, 自然科学研究機構 生理学研究所

ドラマーの最速下肢タッピング速度 - 非ドラマー⾳楽家及び⾮⾳楽家との比較 -

佐田静香, 本田一暁, 藤井進也

[国内会議]  生理研研究会「スポーツからみる日常と非日常の神経生理学」 （オンライン） , 

2022年03月

, 

ポスター発表, 自然科学研究機構 生理学研究所

ドラマーの最速下肢タッピング速度 - 非ドラマー⾳楽家及び⾮⾳楽家との比較 -

佐田静香, 本田一暁, 藤井進也

[国内会議]  第12回東京若手心理学者の会 （オンライン） , 

2022年03月

, 

ポスター発表, 東京若手心理学者の会

ドラムパターン演奏中のメトロノーム同期誤差 - 右下肢⾳楽家ジストニアの症例 -

本田一暁, 佐田静香, 小嶺美月, 山口智史, 柏野牧夫, 藤井進也

[国内会議]  第12回東京若手心理学者の会 （オンライン） , 

2022年03月

, 

ポスター発表, 東京若手心理学者の会

Generalizability Of Sight Vs. Sound Effect: Evidence From Brass Ensemble Competition In Japan

Samma T., Savage P. E., & Fujii S.

[国際会議]  17TH ANNUAL NEUROMUSIC VCONFERENCE: Rhythm And Interpersonal Coordination （Online） , 

2021年11月

, 

ポスター発表, LIVELab at McMaster University

Lower-Limb EMG Activity During Drum-Kit Playing In A Professional Drummer With Dystonia

Honda K., Sata S., Komine K., Yamaguchi S., Kim S.H., & Fujii S.

[国際会議]  17TH ANNUAL NEUROMUSIC VCONFERENCE: Rhythm And Interpersonal Coordination （Online） , 

2021年11月

, 

ポスター発表, LIVELab at McMaster University

動作特異性ジストニアに罹患したドラム奏者の演奏動作中の下肢筋活動に関する症例報告

本田一暁, 佐田静香, 小嶺美月, 山口智史, 金承革, 藤井進也

[国内会議]  第15回 Motor Control研究会 （オンライン） , 

2021年09月

, 

ポスター発表, Motor Control 研究会

自閉傾向が音声質感認識にもたらす影響：時間情報処理に着目して

杉山 翠，藤井 進也，森 将輝

Young Perceptionists' Seminar 2021, 

2021年08月

, 

口頭発表（一般）

Motion Analysis of Finger-bowing Skill in Violin Playing with A High Speed Camera: A Case Study

Fukatsu H., Honda K, Ishizuka T., Ohgi Y., Fujii S.

[国際会議]  ICMPC16-ESCOM11: The 16th International Conference on Music Perception and Cognition / The 11th Triennial Conference of ESCOM, 

2021年07月

, 

ポスター発表

　概要を見る

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.

Human and automated judgements of musical similarity in a global sample

Daikoku H., Shenghao D., Benetos E., Wood A. L. C., Fujii S., Savage P. E.

[国際会議]  ICMPC16-ESCOM11: The 16th International Conference on Music Perception and Cognition / The 11th Triennial Conference of ESCOM, 

2021年07月

, 

ポスター発表

Reliability of automated and human transcriptions of non-Western music

Ozaki Y., McBride J., Benetos E., Pfordresher P., Six J., Tierney A. T., Fujii S., Savage P. E.

[国際会議]  ICMPC16-ESCOM11: The 16th International Conference on Music Perception and Cognition / The 11th Triennial Conference of ESCOM, 

2021年07月

, 

ポスター発表

Are pitches discrete? An information-theoretic framework and a corpus study

McBride J. M., Tierney A. T., Pfordresher P., Six J., Fujii S., Savage P. E.

[国際会議]  ICMPC16-ESCOM11: The 16th International Conference on Music Perception and Cognition / The 11th Triennial Conference of ESCOM, 

2021年07月

, 

ポスター発表

Perceptual vs. automated judgements of music copyright infringement

Yuchen Y., Cronin C., Müllensiefen D., Fujii S., Savage P. E.

[国際会議]  ICMPC16-ESCOM11: The 16th International Conference on Music Perception and Cognition / The 11th Triennial Conference of ESCOM, 

2021年07月

, 

ポスター発表

　概要を見る

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.