Toward Automatic Tuning of the Piano
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A4 Artikkeli konferenssijulkaisussa
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Date
2019
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Mcode
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Language
en
Pages
143-150
Series
Proceedings of the 16th Sound & Music Computing Conference SMC 2019, Proceedings of the Sound and Music Computing Conferences
Abstract
The tuning of a piano is a complicated and time-consuming process, which is usually left for a professional tuner. To make the process faster and non-dependent on the skills of a professional tuner, a semi-automatic piano tuning system is developed. The aim of the system is to help a non-professional person to tune a grand piano with the help of a computer and a motorized tuning machine. The system composes of an aluminum frame, a stepper motor, an Arduino processor, a microphone, and a laptop computer. The stepper motor changes the tuning of the piano strings by turning the pins connected to them whereas the aluminum frame holds the motor in place. The Arduino controls the motor. The microphone and the computer are used as a part of a closed loop control system, which is used to tune the strings automatically. The control system tunes the strings by minimizing the difference between the current and optimal fundamental frequency. The current fundamental frequency is obtained with an inharmonicity coefficient estimation algorithm, and the optimal fundamental frequency is calculated with a novel tuning process, called the Connected Reference Interval (CRI) tuning. With the CRI process, a tuning close to that of a professional tuner is achieved with a deviation of 2.5 cents (RMS) between the keys A0 and G5 and 8.1 cents (RMS) between G#5 and C8, where the tuner’s results are not very consistent.Description
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Citation
Tuovinen, J, Hu, J & Välimäki, V 2019, Toward Automatic Tuning of the Piano . in Proceedings of the 16th Sound & Music Computing Conference SMC 2019 . Proceedings of the Sound and Music Computing Conferences, Sound and Music Computing Association, pp. 143-150, Sound and Music Computing Conference, Malaga, Spain, 28/05/2019 . < http://smc2019.uma.es/articles/S2/S2_02_SMC2019_paper.pdf >
