Violin Sound Quality: Expert Judgements and Objective Measurements

Wrzeciono, Piotr; Marasek, Krzysztof

doi:10.1007/978-3-642-11674-2_11

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…focus on the performers) [2,3] and violins (i.e. focus on the instrument) [36] used machine learning and statistical methods to distinguish good timbre from bad timbre, so we will do the same thing and rely on our "virtual ears" to provide judgements. Concretely, for each simulated note, we define the cost as the RMS of SVM judgements c [h] for all H frames.…”

Section: Validating the Bow Controllermentioning

confidence: 99%

“…To address the problem of controlling the physical models, we turned to machine learning to train the system to perform virtual instruments. Previous uses of machine learning in performance focused on recognizing performer gestures [12], while machine learning has been used off-line to evaluate beginning violinists [2], recognize cellists from their timbre [3], rank violins (rather than violinists) [36], and estimate physical actions from violin audio [25]. Active learning [4] is a technique in machine learning in which the computer generates examples for the user to evaluate; it has been used to generate personalized audio equalizers [21].…”

Section: Related Workmentioning

confidence: 99%

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Physical modelling and supervised training of a virtual string quartet

Percival

Bailey

Tzanetakis

2013

Proceedings of the 21st ACM International Conference on Multimedia

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This work improves the realism of synthesis and performance of string quartet music by generating audio through physical modelling of the violins, viola, and cello. To perform music with the physical models, virtual musicians interpret the musical score and generate actions which control the physical models. The resulting audio and haptic signals are examined with support vector machines, which adjust the bowing parameters in order to establish and maintain a desirable timbre. This intelligent feedback control is trained with human input, but after the initial training is completed, the virtual musicians perform autonomously. The system can synthesize and control different instruments of the same type (e.g., multiple distinct violins) and has been tested on two distinct string quartets (total of 8 violins, 2 violas, 2 cellos). In addition to audio, the system creates a video animation of the instruments performing the sheet music.

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Section: Validating the Bow Controllermentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Physical modelling and supervised training of a virtual string quartet

Percival

Bailey

Tzanetakis

2013

Proceedings of the 21st ACM International Conference on Multimedia

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A listening experiment comparing the timbre of two Stradivari with other violins

Rozzi

Voltini²,

Antonacci

et al. 2022

The Journal of the Acoustical Society of America

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The violins of Stradivari are recognized worldwide as an excellence in craftsmanship, a model for instrument makers, and an unachievable desire for collectors and players. However, despite the myth surrounding these instruments, blindfolded players tendentially prefer to play modern violins. Here, we present a double blind listening experiment aimed at analyzing and comparatively rating the sound timbre of violins. The mythic instruments were listened to among other well regarded and not so well regarded violins. 70 listeners (violin makers of the Cremona area) rated the timbre difference between the simple musical scales played on a test and a reference violin, and the results showed that their preference converged on one particular Stradivari. The acoustical measurements revealed some similarities between the subjective ratings and the physical characteristics of the violins. It is speculated that the myth of Stradivari could have been boosted, among other factors, by the specimens of tonal superior quality, which biased favourably the judgment on his instruments and spread on all of the maker's production. These results contribute to the understanding of the timbre of violins and suggest the characteristics that are in a relationship with the pleasantness of the timbre.

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Violin Sound Quality: Expert Judgements and Objective Measurements

Cited by 2 publications

References 4 publications

Physical modelling and supervised training of a virtual string quartet

Physical modelling and supervised training of a virtual string quartet

A listening experiment comparing the timbre of two Stradivari with other violins

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