Active Scores: Representation and Synchronization in Human–Computer Performance of Popular Music

Dannenberg, Roger B.; Gold, Nicolas; Liang, Dawen; Xia, Guangyu

doi:10.1162/comj_a_00239

Cited by 4 publications

(2 citation statements)

References 8 publications

(10 reference statements)

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“…This supports the concept of a flattened score in which all repeats of the musical score are expanded (Jin, 2013 ). Such a flattened score may be obtained by representing the sheet music using a formal language representation through optical music recognition algorithms, allowing the flattened score to be checked for errors in the interpretation of the repeat instructions (Jin, 2013 ; Dannenberg et al, 2014 ; Ringwalt et al, 2015 ).…”

Section: Related Workmentioning

confidence: 99%

Automated Page Turner for Musicians

Tabone

Bonnici

Cristina

2020

Front. Artif. Intell.

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An increasing number of musicians are opting to use tablet devices instead of traditional print media for their music sheets since the digital medium offers the benefit of storing a lot of music in a compact space. The limited screen size of the tablet devices makes the music difficult to read and musicians often opt to display part of the music page at a time. With fewer music lines on display, the musician will then have to resort to scrolling through the music to read the entire score. This scrolling is annoying since the musicians will need to remove their hands from the instrument to interact with the tablet, causing a break in the music if this is not done quickly enough, or if the tablet is not sufficiently responsive. In this paper, we describe an alternative page turning system which automates the page turning event of the musician. By actively monitoring the musician's on-screen point of regard, the system retains the musician in the loop and thus, the page turns are attuned to the musician's position on the score. By analysing the way the musician's gaze changes between attention to the score and the instrument as well as the way musicians fixate on different parts of the score, we note that musicians often look away from the score and toward their hands, or elsewhere, when playing the instrument. As a result, the eye regions fall outside the field-of-view of the eye-gaze tracker, giving rise to erratic page-turns. To counteract this problem, we create a gaze prediction model that uses Kalman filtering to predict where the musician would be looking on the score. We evaluate our hands-free page turning system using 15 different piano songs containing different levels of difficulty, various repeats, and which also required playing in different registers on the piano, thus, evaluating the applicability of the page-turner under different conditions. Performance of the page-turner was quantified through the number of correct page turns, the number of delayed page turns, and the number of mistaken page turns. Of the 289 page turns involved in the experiment, 98.3% were successfully executed, 1.7% were delayed, while no mistaken page turns were observed.

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Section: Related Workmentioning

confidence: 99%

Automated Page Turner for Musicians

Tabone

Bonnici

Cristina

2020

Front. Artif. Intell.

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“…Finally, the process of defining the architecture brings to the surface issues relevant to the management of notations and representations. The architecture presented here has several components, and a companion article examines some of them in greater detail (Dannenberg et al 2014).…”

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confidence: 99%

Methods and Prospects for Human–Computer Performance of Popular Music

Dannenberg

Gold

Liang

et al. 2014

Computer Music Journal

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Computers are often used in performance of popular music, but most often in very restricted ways, such as keyboard synthesizers where musicians are in complete control, or pre-recorded or sequenced music where musicians follow the computer's drums or click track. An interesting and yet little-explored possibility is the computer as highly autonomous performer of popular music, capable of joining a mixed ensemble of computers and humans. Considering the skills and functional requirements of musicians leads to a number of predictions about future human-computer music performance (HCMP) systems for popular music. We describe a general architecture for such systems and describe some early implementations and our experience with them.Sound and music computing research has made a tremendous impact on music through sound synthesis, audio processing, and interactive systems. In "high art" experimental music, we have also seen important advances in interactive music performance, including computer accompaniment, improvisation, and reactive systems of all sorts. In contrast, few if any systems can claim to support "popular" music performance in genres such as rock, jazz, and folk. Here, we see digital instruments, sequencers, and audio playback, but not autonomous interactive machine performers. Although the practice of popular music may not be a very active topic for research in music technology, it is arguably the dominant form of live music. For example, in a recent weekly listing of concerts in Pittsburgh, there are 24 "classical" concerts, 1 experimental or electro-acoustic performance, and 98 listings for rock, jazz, open stage, and acoustic music.In this article, we explore the approaches to interactive popular music performance with computers. We present a vision for such systems in the form of predictions about future performance

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Personalized Music Improvisation

Kumar

2024

2024 2nd International Conference on Networking and Communications (ICNWC)

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Active Scores: Representation and Synchronization in Human–Computer Performance of Popular Music

Cited by 4 publications

References 8 publications

Automated Page Turner for Musicians

Automated Page Turner for Musicians

Methods and Prospects for Human–Computer Performance of Popular Music

Personalized Music Improvisation

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