Dal Santo, Gloria, De Bortoli, Gian Marco, Prawda, Karolina Anna orcid.org/0000-0003-1026-5486 et al. (2 more authors) (2025) FLAMO: An Open-Source Library for Frequency-Domain Differentiable Audio Processing. In: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE International Conference on Acoustics, Speech and Signal Processing, 06-11 Apr 2025 IEEE , IND
Abstract
We present FLAMO, a Frequency-sampling Library for Audio-Module Optimization designed to implement and optimize differentiable linear time-invariant audio systems. The library is open-source and built on the frequency-sampling filter design method, allowing 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. Practical application of these modules is demonstrated through two case studies: the optimization of an artificial reverberator and an active acoustics system for improved response coloration.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © IEEE 2025. This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
Keywords: | audio processing,delay networks,optimization,machine learning |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 21 May 2025 08:50 |
Last Modified: | 15 Jun 2025 23:03 |
Published Version: | https://doi.org/10.1109/ICASSP49660.2025.10888532 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICASSP49660.2025.10888532 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:226929 |