Dal Santo, Gloria, Prawda, Karolina Anna orcid.org/0000-0003-1026-5486, Schlecht, Sebastian et al. (1 more author) (2025) Optimizing tiny colorless feedback delay networks. EURASIP Journal on Audio, Speech, and Music Processing.
Abstract
A common bane of artificial reverberation algorithms is spectral coloration in the synthesized sound, typically manifesting as metallic ringing, leading to a degradation in the perceived sound quality. In delay network methods, coloration is more pronounced when fewer delay lines are used. This paper presents an optimization framework in which a tiny differentiable feedback delay network, with as few as four delay lines, is used to learn a set of parameters to iteratively reduce coloration. The parameters under optimization include the feedback matrix, as well as the input and output gains. The optimization objective is twofold: to maximize spectral flatness through a spectral loss while maintaining temporal density by penalizing sparseness in the parameter values. A favorable narrow distribution of modal excitation is achieved while maintaining the desired impulse response density. In a subjective assessment, the new method proves effective in reducing perceptual coloration of late reverberation. Compared to the author’s previous work, which serves as the baseline and utilizes a sparsity loss in the time domain, the proposed method achieves computational savings while maintaining performance. The effectiveness of this work is demonstrated through two application scenarios where smooth-sounding synthetic room impulse responses are obtained via the introduction of attenuation filters and an optimizable scattering feedback matrix.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | audio,Audio Engineering,reverberation,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: | 16 May 2025 16:10 |
Last Modified: | 16 May 2025 16:10 |
Published Version: | https://doi.org/10.1186/s13636-025-00401-w |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1186/s13636-025-00401-w |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:226786 |