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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.09126 |
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| _version_ | 1866908486810468352 |
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| author | Mitcheltree, Christopher Teleaga, Bogdan Fyfe, Andrew Masuda, Naotake Schäfer, Matthias Bradic, Alfie Tokui, Nao |
| author_facet | Mitcheltree, Christopher Teleaga, Bogdan Fyfe, Andrew Masuda, Naotake Schäfer, Matthias Bradic, Alfie Tokui, Nao |
| contents | Neural audio processing has unlocked novel methods of sound transformation and synthesis, yet integrating deep learning models into digital audio workstations (DAWs) remains challenging due to real-time / neural network inference constraints and the complexities of plugin development. In this paper, we introduce the Neutone SDK: an open source framework that streamlines the deployment of PyTorch-based neural audio models for both real-time and offline applications. By encapsulating common challenges such as variable buffer sizes, sample rate conversion, delay compensation, and control parameter handling within a unified, model-agnostic interface, our framework enables seamless interoperability between neural models and host plugins while allowing users to work entirely in Python. We provide a technical overview of the interfaces needed to accomplish this, as well as the corresponding SDK implementations. We also demonstrate the SDK's versatility across applications such as audio effect emulation, timbre transfer, and sample generation, as well as its adoption by researchers, educators, companies, and artists alike. The Neutone SDK is available at https://github.com/Neutone/neutone_sdk |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09126 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neutone SDK: An Open Source Framework for Neural Audio Processing Mitcheltree, Christopher Teleaga, Bogdan Fyfe, Andrew Masuda, Naotake Schäfer, Matthias Bradic, Alfie Tokui, Nao Sound Software Engineering Audio and Speech Processing Neural audio processing has unlocked novel methods of sound transformation and synthesis, yet integrating deep learning models into digital audio workstations (DAWs) remains challenging due to real-time / neural network inference constraints and the complexities of plugin development. In this paper, we introduce the Neutone SDK: an open source framework that streamlines the deployment of PyTorch-based neural audio models for both real-time and offline applications. By encapsulating common challenges such as variable buffer sizes, sample rate conversion, delay compensation, and control parameter handling within a unified, model-agnostic interface, our framework enables seamless interoperability between neural models and host plugins while allowing users to work entirely in Python. We provide a technical overview of the interfaces needed to accomplish this, as well as the corresponding SDK implementations. We also demonstrate the SDK's versatility across applications such as audio effect emulation, timbre transfer, and sample generation, as well as its adoption by researchers, educators, companies, and artists alike. The Neutone SDK is available at https://github.com/Neutone/neutone_sdk |
| title | Neutone SDK: An Open Source Framework for Neural Audio Processing |
| topic | Sound Software Engineering Audio and Speech Processing |
| url | https://arxiv.org/abs/2508.09126 |