DiffMoog: a Differentiable Modular Synthesizer for Sound Matching
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913205430779904 |
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| author | Uzrad, Noy Barkan, Oren Elharar, Almog Shvartzman, Shlomi Laufer, Moshe Wolf, Lior Koenigstein, Noam |
| author_facet | Uzrad, Noy Barkan, Oren Elharar, Almog Shvartzman, Shlomi Laufer, Moshe Wolf, Lior Koenigstein, Noam |
| contents | This paper presents DiffMoog - a differentiable modular synthesizer with a comprehensive set of modules typically found in commercial instruments. Being differentiable, it allows integration into neural networks, enabling automated sound matching, to replicate a given audio input. Notably, DiffMoog facilitates modulation capabilities (FM/AM), low-frequency oscillators (LFOs), filters, envelope shapers, and the ability for users to create custom signal chains. We introduce an open-source platform that comprises DiffMoog and an end-to-end sound matching framework. This framework utilizes a novel signal-chain loss and an encoder network that self-programs its outputs to predict DiffMoogs parameters based on the user-defined modular architecture. Moreover, we provide insights and lessons learned towards sound matching using differentiable synthesis. Combining robust sound capabilities with a holistic platform, DiffMoog stands as a premier asset for expediting research in audio synthesis and machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12570 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DiffMoog: a Differentiable Modular Synthesizer for Sound Matching Uzrad, Noy Barkan, Oren Elharar, Almog Shvartzman, Shlomi Laufer, Moshe Wolf, Lior Koenigstein, Noam Audio and Speech Processing Artificial Intelligence Sound This paper presents DiffMoog - a differentiable modular synthesizer with a comprehensive set of modules typically found in commercial instruments. Being differentiable, it allows integration into neural networks, enabling automated sound matching, to replicate a given audio input. Notably, DiffMoog facilitates modulation capabilities (FM/AM), low-frequency oscillators (LFOs), filters, envelope shapers, and the ability for users to create custom signal chains. We introduce an open-source platform that comprises DiffMoog and an end-to-end sound matching framework. This framework utilizes a novel signal-chain loss and an encoder network that self-programs its outputs to predict DiffMoogs parameters based on the user-defined modular architecture. Moreover, we provide insights and lessons learned towards sound matching using differentiable synthesis. Combining robust sound capabilities with a holistic platform, DiffMoog stands as a premier asset for expediting research in audio synthesis and machine learning. |
| title | DiffMoog: a Differentiable Modular Synthesizer for Sound Matching |
| topic | Audio and Speech Processing Artificial Intelligence Sound |
| url | https://arxiv.org/abs/2401.12570 |