---
title: "Predicting Spectral Information for Self-Supervised Signal Classification"
authors:
  - "Yi Xu"
  - "Shuang Wang"
  - "Hantong Xing"
  - "Chenxu Wang"
  - "Dou Quan"
  - "Rui Yang"
  - "Dong Zhao"
  - "Luyang Mei"
doi: "10.24963/ijcai.2025/752"
url: "https://ijcai.org/proceedings/2025/752"
pdf: "https://ijcai.org/proceedings/2025/0752.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "6758-6766"
track: "Main Track"
keywords:
  - "Machine Learning: ML: Time series and data streams"
  - "Machine Learning: ML: Classification"
  - "Machine Learning: ML: Self-supervised Learning"
  - "Machine Learning: ML: Representation learning"
---

# Predicting Spectral Information for Self-Supervised Signal Classification

Yi Xu, Shuang Wang, Hantong Xing, Chenxu Wang, Dou Quan, Rui Yang, Dong Zhao, Luyang Mei

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 6758-6766. DOI: [10.24963/ijcai.2025/752](https://doi.org/10.24963/ijcai.2025/752)

## Abstract

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.