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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.14390 |
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| _version_ | 1866912723984449536 |
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| author | Yu, Chin-Yun Fazekas, György |
| author_facet | Yu, Chin-Yun Fazekas, György |
| contents | We introduce a general formulation for automatic differentiation through direct form filters, yielding a closed-form backpropagation that includes initial condition gradients. The result is a single expression that can represent both the filter and its gradients computation while supporting parallelism. C++/CUDA implementations in PyTorch achieve at least 1000x speedup over naive Python implementations and consistently run fastest on the GPU. For the low-order filters commonly used in practice, exact time-domain filtering with analytical gradients outperforms the frequency-domain method in terms of speed. The source code is available at https://github.com/yoyolicoris/philtorch. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14390 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating Automatic Differentiation of Direct Form Digital Filters Yu, Chin-Yun Fazekas, György Systems and Control Audio and Speech Processing Signal Processing We introduce a general formulation for automatic differentiation through direct form filters, yielding a closed-form backpropagation that includes initial condition gradients. The result is a single expression that can represent both the filter and its gradients computation while supporting parallelism. C++/CUDA implementations in PyTorch achieve at least 1000x speedup over naive Python implementations and consistently run fastest on the GPU. For the low-order filters commonly used in practice, exact time-domain filtering with analytical gradients outperforms the frequency-domain method in terms of speed. The source code is available at https://github.com/yoyolicoris/philtorch. |
| title | Accelerating Automatic Differentiation of Direct Form Digital Filters |
| topic | Systems and Control Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2511.14390 |