Piano sound synthesis has been an active topic of research and development for several decades. Although comprehensive physics-based piano models have been proposed, sample-based piano emulation is still widely utilized for its computational efficiency and relative accuracy despite presenting significant memory storage requirements. This paper proposes a novel hybrid approach to sample-based piano synthesis aimed at improving the fidelity of sound emulation while reducing memory requirements for storing samples. A neural network-based model processes the sound recorded from a single piano key at a given velocity. The network is trained to learn the nonlinear relationship between the various velocities at which a piano key is pressed and the corresponding sound alterations. Results show that the method achieves high accuracy using a specific neural architecture that is computationally efficient, presenting few trainable parameters, and it requires memory only for one sample for each piano key.
@inproceedings{simionato_neuralpcm_2025,
author = {Simionato Riccardo and Fasciani Stefano},
title = {Neural Sampled-based Piano Synthesis},
booktitle = {Proceedings of the International Conference on Digital Audio Effects (DAFx25)},
year = {2025},
address = {Ancona, Italy},
}