{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:04:45Z","timestamp":1773803085265,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"27","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Robust signal enhancement under non-stationary and low SNR conditions remains challenging, as methods based on the short-time Fourier transform (STFT) with fixed resolution struggle to represent complex and time\u2013frequency structures. While leveraging the fractional domain as an auxiliary view offers flexibility in modeling time-frequency structures, existing methods typically adopt fixed transform orders and overlook alignment between views, hindering effective integration of complementary representations and leaving frequency domain misalignment unresolved. Therefore, we propose FracFusion, a novel framework that integrates a learnable short-time fractional Fourier Transform (STFrFT) module to generate dynamic auxiliary views, combined with two stage alignment-aware fusion modules: Pearson Channel Fusion for correlation-guided consistency and Efficient Align Fusion for fine-grained, frequency aligned interaction. Experiments on speech and electromagnetic (EM) datasets show that FracFusion consistently outperforms state-of-the-art baselines across diverse noise levels and signal types, demonstrating robust adaptability across domains.<\/jats:p>","DOI":"10.1609\/aaai.v40i27.39404","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:30:52Z","timestamp":1773797452000},"page":"22454-22462","source":"Crossref","is-referenced-by-count":0,"title":["Signal Enhancement via Multi-view Dynamic Representation and Alignment-aware Fusion"],"prefix":"10.1609","volume":"40","author":[{"given":"Zikun","family":"Jin","sequence":"first","affiliation":[]},{"given":"Yuhua","family":"Qian","sequence":"additional","affiliation":[]},{"given":"Xinyan","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Jiaqian","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jinpeng","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Shen","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Haijun","family":"Geng","sequence":"additional","affiliation":[]},{"given":"Honghong","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39404\/43365","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39404\/43365","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:30:52Z","timestamp":1773797452000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/39404"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"27","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i27.39404","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}