Optimizing tiny colorless feedback delay networks
Dal Santo G, Prawda K, Schlecht SJ, Välimäki V (2025)
Publication Type: Journal article
Publication year: 2025
Journal
Book Volume: 2025
Article Number: 13
Journal Issue: 1
DOI: 10.1186/s13636-025-00401-w
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.
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Finland (FI)How to cite
APA:
Dal Santo, G., Prawda, K., Schlecht, S.J., & Välimäki, V. (2025). Optimizing tiny colorless feedback delay networks. EURASIP Journal on Audio, Speech, and Music Processing, 2025(1). https://doi.org/10.1186/s13636-025-00401-w
MLA:
Dal Santo, Gloria, et al. "Optimizing tiny colorless feedback delay networks." EURASIP Journal on Audio, Speech, and Music Processing 2025.1 (2025).
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