An effective method for audio-to-score alignment using onsets and modified constant Q spectra

Chen, Chunta; Jang, Jyh-Shing Roger

doi:10.1007/s11042-018-6349-y

Cited by 2 publications

(7 citation statements)

References 22 publications

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“…Thus, this approach is not suitable for page-turning applications, which requires real-time alignment of the two. Chen and Jang (2019) propose an audio-score alignment process based on a similar approach. Note onsets are detected from the audio signal, extracting a feature vector to describe the signal around each onset.…”

Section: Score-following Systemsmentioning

confidence: 99%

“…The musician can also introduce temporal changes within the music as a means of expression. These tempo changes would typically affect the estimation of the note onsets (Chen and Jang, 2019 ). Unintended changes to the performance are also possible, depending on the skill level of the performer.…”

Section: Related Workmentioning

confidence: 99%

“…Chen and Jang ( 2019 ) propose an audio-score alignment process based on a similar approach. Note onsets are detected from the audio signal, extracting a feature vector to describe the signal around each onset.…”

Section: Related Workmentioning

confidence: 99%

“…This measure allows for invariance to instrument timbre and overtone interference. To allow for the online score-following, Chen and Jang ( 2019 ) then modify the algorithm to reduce the computational time required to align the audio to the score. To achieve this, they assume performance stability and performance continuity.…”

Section: Related Workmentioning

confidence: 99%

“…They also limit the computation of concurrencies to onsets around the previously matched concurrencies. Chen and Jang ( 2019 ) achieve a mean latency of 19.2 ms obtained from 10 pre-recorded, human-played, four-part chorales composed by Bach.…”

Section: Related Workmentioning

confidence: 99%

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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: Score-following Systemsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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