{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:15:29Z","timestamp":1758672929920,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Deep learning methods have demonstrated remarkable performance across various communication signal processing tasks. However, most signal classification methods require a substantial amount of labeled samples for training, posing significant challenges in the field of communication signals, as labeling necessitates expert knowledge. This paper proposes a novel self-supervised signal classification method called Spectral-Guided Self-Supervised Signal Classification (SGSSC). Specifically, to leverage frequency-domain information with modulation semantics as prior knowledge for the model, we design a previously unexplored pretext task tailored to the format of signal data. This task involves predicting spectral information from masked time-domain signals, enabling the model to learn implicit signal features through cross-domain pattern transformation. Furthermore, the pretext task in the SGSSC method is relevant to the downstream classification task, and using traditional fine-tuning strategies on the downstream task may lead to the loss of certain features associated with the pretext task. Therefore, we propose an attention mechanism-based fine-tuning strategy that adaptively integrates pre-trained features from different levels. Extensive experimental results validate the superiority of the SGSSC method. For instance, when the proportion of labeled samples is only 0.5%, our method achieves an average improvement of 2.3% in downstream classification tasks compared to the best-performing self-supervised training strategies.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/752","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"6758-6766","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Spectral Information for Self-Supervised Signal Classification"],"prefix":"10.24963","author":[{"given":"Yi","family":"Xu","sequence":"first","affiliation":[{"name":"Xidian University"}]},{"given":"Shuang","family":"Wang","sequence":"additional","affiliation":[{"name":"Xidian University"}]},{"given":"Hantong","family":"Xing","sequence":"additional","affiliation":[{"name":"Xidian University"}]},{"given":"Chenxu","family":"Wang","sequence":"additional","affiliation":[{"name":"Xidian University"}]},{"given":"Dou","family":"Quan","sequence":"additional","affiliation":[{"name":"Xidian University"}]},{"given":"Rui","family":"Yang","sequence":"additional","affiliation":[{"name":"Xidian University"}]},{"given":"Dong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Xidian University"}]},{"given":"Luyang","family":"Mei","sequence":"additional","affiliation":[{"name":"Xidian University"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:35:03Z","timestamp":1758627303000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/752"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/752","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}