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In this study the authors looked at the ability of artificial intelligence to detect tempo, rhythm, and intonation of a piece played on violin. Technology such as this would allow for students to practice and get feedback without the need of a teacher.

Vibrato, defined as a rapid and subtle oscillation in pitch, is a technique that is commonly used by musicians to add expression and colour to notes. However, on stringed instruments, there are certain notes (open string notes) on which it is impossible to perform the technique. Without vibrato, they can sound angular and unpleasant, especially when juxtaposed against other notes played with vibrato. String players therefore use an alternative to achieve the same vibrato effect on the open string — a technique referred to as “open string vibrato”. While the technique is widely used, it is unknown how much of a physical effect it has on the sound waves produced, if any at all. The purpose of this study is to analyse open string vibrato using a statistical approach to provide evidence to characterize the physical effect of the technique, and then compare it to normal vibrato. We hypothesised that it would have a noticeable and measurable effect on the sound waves produced because of the technique’s widespread usage. To test this, notes, with and without either open string vibrato or normal vibrato, were recorded on the violin. We analyzed the audio recordings using a computational and statistical approach. The results of the study partially agreed with our hypothesis: while the technique has an observable physical effect on the sound waves, the effect is weaker than expected. We concluded that open string vibrato does work, but has quite a subtle effect, and thus should only be used when there is no other option.

Investigation of resonant vibration using cello model