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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/2505.07709 |
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| _version_ | 1866909607814758400 |
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| author | Haider, Daniel Perfler, Felix Balazs, Peter Hollomey, Clara Holighaus, Nicki |
| author_facet | Haider, Daniel Perfler, Felix Balazs, Peter Hollomey, Clara Holighaus, Nicki |
| contents | This paper introduces ISAC, an invertible and stable, perceptually-motivated filter bank that is specifically designed to be integrated into machine learning paradigms. More precisely, the center frequencies and bandwidths of the filters are chosen to follow a non-linear, auditory frequency scale, the filter kernels have user-defined maximum temporal support and may serve as learnable convolutional kernels, and there exists a corresponding filter bank such that both form a perfect reconstruction pair. ISAC provides a powerful and user-friendly audio front-end suitable for any application, including analysis-synthesis schemes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ISAC: An Invertible and Stable Auditory Filter Bank with Customizable Kernels for ML Integration Haider, Daniel Perfler, Felix Balazs, Peter Hollomey, Clara Holighaus, Nicki Sound Machine Learning This paper introduces ISAC, an invertible and stable, perceptually-motivated filter bank that is specifically designed to be integrated into machine learning paradigms. More precisely, the center frequencies and bandwidths of the filters are chosen to follow a non-linear, auditory frequency scale, the filter kernels have user-defined maximum temporal support and may serve as learnable convolutional kernels, and there exists a corresponding filter bank such that both form a perfect reconstruction pair. ISAC provides a powerful and user-friendly audio front-end suitable for any application, including analysis-synthesis schemes. |
| title | ISAC: An Invertible and Stable Auditory Filter Bank with Customizable Kernels for ML Integration |
| topic | Sound Machine Learning |
| url | https://arxiv.org/abs/2505.07709 |