FLAMO

Frequency-sampling Library for Audio-Module Optimization (FLAMO) is an open-source frequecy-sampling library for audio-module optimization in PyTorch. FLAMO is designed to implement and optimize differentiable linear time-invariant audio systems and is built on the frequency-sampling filter design method. It allows for the creation of differentiable modules that can be used stand-alone or within the computation graph of neural networks, simplifying the development of differentiable audio systems. It includes predefined filtering modules and auxiliary classes for constructing, training, and logging the optimized systems, all accessible through an intuitive interface. The code can be found in this github repository.

Installation

To install FlAMO via pip, on a new python virtual environment flamo-env

python3.10 -m venv .flamo-env
source .flamo-env/bin/activate
pip install flamo

If you are using conda, you might need to install libsndfile manually

conda create -n flamo-env python=3.10
conda activate flamo-env
pip install flamo
conda install -c conda-forge libsndfile

For local installation: clone and install dependencies on a new pyton virtual environment flamo-env

git clone https://github.com/gdalsanto/flamo
cd flamo
python3.10 -m venv .flamo-env
source .flamo-env/bin/activate
pip install -e .

Note that it requires python>=3.10

Contents

The main code is contained in processor and is divided in two parts:

  • dsp contains the differentiable systems (e.g., filters, delays) and basic transformations (e.g. FFT, IFFT)

  • system contains classes that allow to build differentiable systems (i.e. Series, Recursion) and other utility classes (i.e. Shell).

Processor

The optimize module contains classes for handling training, validation, results logging, etc. It comes with a set of datasets and loss functions.

Optimization

The functional module is a collector of signal processing function and transformations that can be used as, e.g., parameters mappings.

Functional

The auxiliary module is a collector of auxiliary function and modules that showcase and expand applications of the library.

Auxiliary

Reference and Contacts

If you use FLAMO in your research, please cite the following conference paper:

  • Gloria Dal Santo, Gian Marco De Bortoli, Karolina Prawda, Sebastian Jiro Schlecht, Vesa Välimäki “FLAMO: An Open-Source Library for Frequency-Domain Differentiable Audio Processing”, in Proceedings of the 2025 International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Hyderabad, India, 2025.

For any questions or issues, please contact

or open an issue on the github repository.

License

FLAMO is licensed under the MIT License.

MIT License

Copyright (c) 2024 Gloria Dal Santo, Gian Marco De Bortoli, Sebastian Jiro Schlecht

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

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