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

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.

@article{haider2025_2505.07709,
  title={ ISAC: An Invertible and Stable Auditory Filter Bank with Customizable Kernels for ML Integration },
  author={ Daniel Haider and Felix Perfler and Peter Balazs and Clara Hollomey and Nicki Holighaus },
  journal={arXiv preprint arXiv:2505.07709},
  year={ 2025 }
}