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HybrA-Filterbanks
=================
.. figure:: https://github.com/danedane-haider/HybrA-Filterbanks/blob/main/HybrA.png
   :alt: Logo

   Logo

About
-----

This repository contains the official implementaions of `Hybrid Auditory
filterbanks <https://arxiv.org/abs/2408.17358>`__ and
`ISAC <https://arxiv.org/abs/2505.07709>`__. The modules are designed to
be easily usable in the design of PyTorch model designs.

Documentation
-------------

`https://github.com/danedane-haider/HybrA-Filterbanks <https://danedane-haider.github.io/HybrA-Filterbanks/main/>`__

Installation
------------

We publish all releases on PyPi. You can install the current version by
running:

::

   pip install hybra

Usage
-----

This package offers several PyTorch modules to be used in your code
performing transformations of an input signal into a time frequency
representation.

.. code:: python

   from hybra import HybrA

   import soundfile
   import torch

   device = "mps"

   x, fs = soundfile.read("./audio/crush.wav")
   x = 2 * torch.tensor(x[:, 0], dtype=torch.float32).unsqueeze(0)
   sig_len = x.shape[-1]

   filterbank = HybrA(L=sig_len,stride=8,scale='mel').to(device)
   filterbank.plot_response()

   out = filterbank(x.to(device))

Citation
--------

If you find our work valuable, please cite

::

   @article{HaiderTight2024,
     title={Hold me Tight: Trainable and stable hybrid auditory filterbanks for speech enhancement},
     author={Haider, Daniel and Perfler, Felix and Lostanlen, Vincent and Ehler, Martin and Balazs, Peter},
     journal={arXiv preprint arXiv:2408.17358},
     year={2024}
   }
   @article{HaiderISAC2025,
         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},
         year={2025},
         url={arXiv preprint arXiv:2505.07709}, 

   }



.. toctree::
   :maxdepth: 2
   :caption: Contents:

   api