Violin Timbre Navigator: Real-Time Visual Feedback of Violin Bowing Based on Audio Analysis and Machine Learning

Carrillo, Alfonso Perez

doi:10.1007/978-3-030-05716-9_15

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…Nonetheless, acoustic features of string sound can provide information about the means of sound production. For example, machine learning models have been successfully applied to the prediction of violin bowing parameters (Perez-Carrillo, 2019), and assessing violin tone quality (Giraldo, Ramirez, Waddell, & Williamon, 2017;Giraldo et al, 2019) from acoustic features of the sound. Acoustic analysis has also been used to characterise the sound of different violins (Nagyvary, 2013), and to predict cello performer identity (Chudy, 2016).…”

Section: Sound Production In Violin Playingmentioning

confidence: 99%

Motor performance in violin bowing: Effects of attentional focus on acoustical, physiological and physical parameters of a sound-producing action

Allingham

Burger

Wöllner

2021

Journal of New Music Research

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Violin bowing is a specialised sound-producing action, which may be affected by psychological performance techniques. In sport, attentional focus impacts motor performance, but limited evidence for this exists in music. We investigated effects of attentional focus on acoustical, physiological, and physical parameters of violin bowing in experienced and novice violinists. Attentional focus significantly affected spectral centroid, bow contact point consistency, shoulder muscle activity, and novices' violin sway. Performance was most improved when focusing on tactile sensations through the bow (somatic focus), compared to sound (external focus) or arm movement (internal focus). Implications for motor performance theory and pedagogy are discussed.

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Section: Sound Production In Violin Playingmentioning

confidence: 99%

Motor performance in violin bowing: Effects of attentional focus on acoustical, physiological and physical parameters of a sound-producing action

Allingham

Burger

Wöllner

2021

Journal of New Music Research

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“…A more recent European Commission project, Technology Enhanced Learning of Music Instruments (TELMI, 2016–2019) included the design and implementation of new interaction paradigms for music learning and training based on state-of-the-art technologies (Kholykhalova et al, 2017 ; Ortega et al, 2017 ; Giraldo et al, 2019 ; Perez-Carrillo, 2019 ). The project focused primarily on violin performance, with the development of a prototype tool called SkyNote that can provide real-time feedback on pitch and intonation, dynamics, tone quality, and rhythm.…”

Section: Review Of Musical Instrument Educational Technologiesmentioning

confidence: 99%

Current State and Future Directions of Technologies for Music Instrument Pedagogy

Acquilino

Scavone

2022

Front. Psychol.

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Technological advances over the past 50 years or so have resulted in the development of a succession of hardware and software systems intended to improve the quality and effectiveness of Western music instrument pedagogy during classroom instruction or individual study. These systems have aimed to provide evaluation or visualization of single or combined technical aspects by analyzing performance data collected in real time or offline. The number of such educational technologies shows an ever-increasing trend over time, aided by the wide diffusion and availability of mobile devices. However, we believe there are unrealized opportunities for modern technologies to help music students in their technical development and assist them during their practice sessions in between visits to their teachers. The ubiquity of PCs and mobile devices with built-in microphones, speakers, and cameras has inspired the development of media technologies in support of music pedagogy. They offer an attractive potential for implementing audio signal processing algorithms addressing different technical skills of the performer, providing real-time feedback, collecting data over time, and applying statistical models. Despite this potential, most available software for music instrument pedagogy remains very limited in functionality. This study provides a survey of music edTech software available, together with the methods of use, addressed technical skills, commonalities, and limitations. Results show that most current software is based on the metronome and tuner, with only a few systems that have limited abilities to follow a performance in real-time and compare it to a given score to monitor correctness of notes, intonation, and rhythm. The survey also highlights a high and under-exploited potential regarding the monitoring of other more specific technical skills, which are more instrument-dependent, but no less important, such as the control of dynamic range and clarity of the attack. This article ends with a discussion of possible directions for future development of technologies to support the practice of music students at different levels, with some consideration for the corresponding signal processing methods that can be utilized or that need advancement. By helping students to more efficiently achieve a high level of proficiency of their instruments with assistive technologies, we hope to minimize stress and afford better enjoyment of the music performance experience for all.

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“…We selected features that are either commonly used in the literature in related tasks, or have been validated in the context of violin bowing technique recognition in [2]. Six timbre related features are considered.…”

Section: Timbre Feature Extractionmentioning

confidence: 99%

“…In a previous study [1], bowing data were acquired and measured using a hardware systems. However, the use of expensive sensing systems and complex setups are often intrusive in practice.Timbre features extracted from audio are capable of characterising violin bowing parameters to a good extent [2], while timbre variations are characteristic of a performer's individual preference and personal style [3].…”

Section: Introductionmentioning

confidence: 99%

Violinist Identification Using Note-Level Timbre Feature Distributions

Zhao

Fazekas

Sandler

2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Modelling musical performers' individual playing styles based on audio features is important for music education, music expression analysis and music generation. In violin performance, the perception of playing styles are mainly affected by the characteristic musical timbre, which is mostly determined by performers, instruments and recording conditions. To verify if timbre features can describe a performer's style adequately, we examine a violinist identification method based on note-level timbre feature distributions. We first apply it using solo datasets to recognise professional violinists, then use it to identify master players from commercial concerto recordings. The results show that the designed features and method work very well for both datasets. The identification accuracy with the solo dataset using MFCCs and spectral contrast features are 0.94 and 0.91 respectively. Significantly lower but promising results are reported with the concerto dataset. Results suggest that the selected timbre features can model performers' individual playing reasonably objectively, regardless of the instrument they play.

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Violin Timbre Navigator: Real-Time Visual Feedback of Violin Bowing Based on Audio Analysis and Machine Learning

Cited by 3 publications

References 11 publications

Motor performance in violin bowing: Effects of attentional focus on acoustical, physiological and physical parameters of a sound-producing action

Motor performance in violin bowing: Effects of attentional focus on acoustical, physiological and physical parameters of a sound-producing action

Current State and Future Directions of Technologies for Music Instrument Pedagogy

Violinist Identification Using Note-Level Timbre Feature Distributions

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