Paper Archive

The acoustic piano and its sound production mechanisms have been extensively studied in the field of acoustics. Similarly, digital piano synthesis has been the focus of numerous signal processing research studies. However, the role of the piano action in shaping the dynamics and nuances of piano sound has received less attention, particularly in the context of digital pianos. Digital pianos are well-established commercial instruments that typically use weighted keys with two or three sensors to measure the average key velocity—this being the only input to a sampling synthesis engine. In this study, we investigate whether this simplified measurement method adequately captures the full dynamic behavior of the original piano action. After a brief review of the state of the art, we describe an experimental setup designed to measure physical properties of the keys and hammers of a piano. This setup enables high-precision readings of acceleration, velocity, and position for both the key and hammer across various dynamic levels. Through extensive data analysis, we examine their relationships and identify the optimal key position for velocity measurement. We also analyze a digital piano key to determine where the average key velocity is measured and compare it with our proposed optimal timing. We find that the instantaneous key velocity just before let-off correlates most strongly with hammer impact velocity, indicating a target for improved sensing; however, due to the limitations of discrete velocity sensing this optimization alone may not suffice to replicate the nuanced expressiveness of acoustic piano touch. This study represents the first step in a broader research effort aimed at linking piano touch, dynamics, and sound production.

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Recent advances in AI-assisted coding tools raise questions about how programming-intensive subjects such as audio digital signal processing should be taught and how exam projects should be evaluated. This paper presents a small-scale controlled exploratory study conducted in a graduate course on music DSP. As a final project at the end of the course, the students implemented a modular synthesizer plugin in C++. Half of the students had access to AI-assisted coding support, while the other group developed the plugin manually. All students had to follow a protocol and provide data at the end of the project, together with their code, which was discussed with them as part of the course exam. Although the scale of the study is small, the paper shares a qualitative analysis of the results and a few takeaway messages for future reference among lecturers in the field. Overall, AI-assisted coding does provide some advantage to students but only in certain regards. The used AI tools, trained on GitHub repositories, seem to have only partial awareness of the state of the art in digital audio processing (e.g. antialiasing oscillators, virtual analog filters, etc.). Finally, the use of AI seems to not interfere excessively with the ability of the students to learn from their practical experience.

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