  This paper introduces a nonlinear string sound synthesizer, based on a finite
difference simulation of the dynamic behavior of strings under various
excitations. The presented synthesizer features a versatile string simulation
engine capable of stochastic parameterization, encompassing fundamental
frequency modulation, stiffness, tension, frequency-dependent loss, and
excitation control. This open-source physical model simulator not only benefits
the audio signal processing community but also contributes to the burgeoning
field of neural network-based audio synthesis by serving as a novel dataset
construction tool. Implemented in PyTorch, this synthesizer offers flexibility,
facilitating both CPU and GPU utilization, thereby enhancing its applicability
as a simulator. GPU utilization expedites computation by parallelizing
operations across spatial and batch dimensions, further enhancing its utility
as a data generator.
