Hardware Design and Development of Contactless Sensor System for Piano Playing

Abstract

AbstractThe art of planning the movement of hands in order to produce the desired sound of the piano is one of the important parts of piano technique. Various researches had attempted to unveil the technique of virtuoso pianists using technologies. These researches employ different types of sensors in order to capture motion data in piano playing. However, one area in this research had been under-represented, which is the finger position of the musician while playing the musical instrument. In piano technique, it is very important to study the finger position that could land on any position along the surface of one single key. Researches that embark on this area faced a common problem, the sensors used in these researches are directly in contact with the pianist, which causes a change of piano playing experience. Since piano playing consists of very delicate interaction between the pianist and the piano, such change of experience may affect the pianist’s performance. These sensors are considered to be intrusive to the piano playing experience. Concluding the challenges faced by current technologies, a non-intrusive and long-range capacitive sensor is developed. This sensor employs the RC oscillator method where the change of the capacitance is recorded in number of pulses. The size and shape of electrode is designed to be able to sense the required distance. This sensor is placed right under keyboard area of a piano to detect changes in capacitance when user’s finger approaches the keyboard. In this chapter, a prototype sensor is developed to sense different positions of the fingers on five keys of the piano out of the entire 88 keys. To validate the design, input data with known output position were collected and fed into an artificial neural network for training. The output of the neural network is shown in regression plots, where the overall coefficient of determination, R = 0.96747. Finally, another 500 sets of independent input data were used to test the network and the output shows R = 0.92004, with approximately 90% accuracy. The fit value and the accuracy are reasonably good for the data set. The output data represents the position of the fingers on the piano keyboard with approximately 7 mm deviation. This work enables pianists to store their piano playing behavior and piano technique data that could be easily shared to different users in digital form. Since each pianist plays the piano differently, this system could be used for storing and preserving a pianist’s piano technique.