{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:23Z","timestamp":1761176123315,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>We introduce N-GAN, an end-to-end GAN architecture for neural audio super resolution that can accommodate multiple input sample rates. We refer to our approach as \u201cWave-to-Wave\u201d to distinguish it from the \u201cWave-to-Spectrogram-to-Wave\u201d and \u201cWave-and-Spectrogram-to-Wave\u201d approaches upon which the state-of-the-art results on this task are based. Our proposed \u201cWave-to-Wave\u201d architecture produces models that are orders of magnitude smaller than current state-of-the-art models whilst matching or exceeding their performance. In addition, our approach improves inference speed by at least 150% (2.5x speedup) over previous similarly performant models. We show that our model obtains state-of-the-art performance on a target sample rate of 48kHz and input sample rates of 8kHz, 16kHz and 24kHz.<\/jats:p>","DOI":"10.3233\/faia250821","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:43:13Z","timestamp":1761126193000},"source":"Crossref","is-referenced-by-count":0,"title":["N for Parameter: Efficient Multi-Scale Neural Audio Super Resolution with GAN"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2060-8625","authenticated-orcid":false,"given":"Mark","family":"Magumba","sequence":"first","affiliation":[{"name":"Center for Sustainable Digital Technologies, Technological University Dublin"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3300-1152","authenticated-orcid":false,"given":"Steven","family":"Davy","sequence":"additional","affiliation":[{"name":"Center for Sustainable Digital Technologies, Technological University Dublin"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Owais","family":"bin Zuber","sequence":"additional","affiliation":[{"name":"Huawei Ireland Research Center"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250821","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:43:14Z","timestamp":1761126194000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250821"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250821","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}