A Deep Learning-Driven Approach for Musical Instrument Sound Synthesis and Acoustic Optimization

This structured narrative review examines deep‑learning‑driven musical instrument timbre synthesis, covering playing‑condition control, acoustic defect diagnosis, structural inverse design, and subjective‑objective quality evaluation. Adopting a structured narrative review method, this work follows the main themes of technical paradigms, application closed loops, supporting resources, evaluation evidence, and industrial constraints. Relevant methods are organized as direct fitting of waveforms or spectrograms to hybrid modeling embedded with physical priors, and further toward interpretable end‑to‑end systems built upon differentiable digital signal processing (DDSP) and differentiable physical modeling. A closed‑loop research framework of "multimodal evidence‑physical constraint‑perceptual evaluation‑human‑in‑the‑loop" is proposed in this paper. The major findings are as follows: pure data‑driven models possess strong representation capability yet suffer from data bias and uncontrolled extrapolation; physical priors can boost sample efficiency and parameter interpretability, whereas their benefits depend on the accuracy of priors and optimization stability; differentiable acoustics provides a unified interface for synthesis, inversion, and design, but still encounters challenges including high‑frequency gradient pathology, multi‑physics coupling, real‑time computation and incomplete perceptual objectives. Future research directions include cross‑cultural musical‑instrument datasets, digital twins with uncertainty estimation, auditable auditory preference models, low‑latency edge‑end deployment, and governance of copyright and performer rights. This study provides a unified conceptual framework and reusable evaluation pathways for music‑acoustics researchers, algorithm developers, and lutherie engineers.