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Despite significant advancements, automatic parameter tuning of delay-network models remains an open challenge. We introduce a novel method for finding the parameters of a feedback delay network (FDN) such that its output renders target attributes of a measured room impulse response. The proposed approach involves the implementation of a differentiable FDN with trainable delay lines, which, for the first time, allows us to simultaneously learn each and every delay-network parameter via backpropagation. The iterative optimization process seeks to minimize a perceptually motivated time-domain loss function incorporating differentiable terms accounting for energy decay and echo density. Through experimental validation, we show that the proposed method yields time-invariant frequency-independent FDNs capable of closely matching the desired acoustical characteristics and outperforms existing methods based on genetic algorithms and analytical FDN design.<\/jats:p>","DOI":"10.1186\/s13636-024-00371-5","type":"journal-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T06:04:57Z","timestamp":1728367497000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Data-driven room acoustic modeling via differentiable feedback delay networks with learnable delay lines"],"prefix":"10.1186","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9438-3190","authenticated-orcid":false,"given":"Alessandro Ilic","family":"Mezza","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6144-8288","authenticated-orcid":false,"given":"Riccardo","family":"Giampiccolo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8007-4370","authenticated-orcid":false,"given":"Enzo","family":"De Sena","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7973-0134","authenticated-orcid":false,"given":"Alberto","family":"Bernardini","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,8]]},"reference":[{"issue":"4","key":"371_CR1","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1109\/JPROC.2011.2182069","volume":"100","author":"JG Apostolopoulos","year":"2012","unstructured":"J.G. 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