A hybrid architecture combining physical modeling and neural networks for piano sound synthesis

This paper presents a novel hybrid architecture for piano sound synthesis that combines physical modeling and neural networks. The proposed approach leverages the strengths of both methods by integrating a physical modeling module that simulates the acoustic behavior of piano components with a neural network module that learns complex timbral mappings. The physical modeling module provides a physically grounded foundation by modeling string vibrations, hammer-string interactions, and soundboard radiation, while the neural network captures subtle tonal characteristics through data-driven learning. Experimental results demonstrate that our hybrid architecture achieves superior performance in terms of sound quality, realism, and expressiveness compared to traditional synthesis methods, while maintaining reasonable computational efficiency. Objective evaluations show improved spectral accuracy with a spectral convergence score of 0.92, and subjective listening tests confirm enhanced perceptual quality with realism scores of 4.6 and expressiveness scores of 4.5 on a 5-point scale. The hybrid approach achieves synthesis in 2.5 seconds with 1200 MB memory usage, demonstrating computational efficiency. The architecture enables intuitive control through physical parameters while benefiting from the learning capabilities of neural networks, making it suitable for various applications in music production and performance.