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arXiv · 2505.07709

ISAC: An Invertible and Stable Auditory Filter Bank with Customizable Kernels for ML Integration

Daniel Haider·Felix Perfler·Peter Balazs·Clara Hollomey·Nicki Holighaus
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Abstract

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.

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Daniel Haider, Felix Perfler, Peter Balazs, Clara Hollomey, Nicki Holighaus. 2025-05-12. ISAC: An Invertible and Stable Auditory Filter Bank with Customizable Kernels for ML Integration. https://arxiv.org/abs/2505.07709

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