String Sound Synthesizer on GPU-accelerated Finite Difference Scheme
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911749925502976 |
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| author | Lee, Jin Woo Choi, Min Jun Lee, Kyogu |
| author_facet | Lee, Jin Woo Choi, Min Jun Lee, Kyogu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_18505 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | String Sound Synthesizer on GPU-accelerated Finite Difference Scheme Lee, Jin Woo Choi, Min Jun Lee, Kyogu Sound Audio and Speech Processing Signal Processing 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. |
| title | String Sound Synthesizer on GPU-accelerated Finite Difference Scheme |
| topic | Sound Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2311.18505 |