Zenghao Duan, Zhiyi Yin, Zhichao Shi, Liang Pang, Shaoling Jing, Zihe Huang, Jiayi Wu, Yu Yan, Jingcheng Deng, Huawei Shen, Xueqi Cheng. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Xiucheng Xu, Bingbing Xu, Tian Xueyun, Zihe Huang, Rongxin Chen, Li Yunfan, Huawei Shen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Robust audio fingerprinting plays a crucial role in applications such as audio authentication and copyright protection. However, existing fingerprinting techniques often rely heavily on hand-crafted features and struggle to maintain robustness and generalization, particularly when dealing with long-duration audios and facing complex distortions. To address these challenges, we propose a deep learning framework for robust audio fingerprinting with multi-feature enhancement. First, origin audio is transformed into Log-Mel spectrum, and time-frequency features are extracted by a convolutional neural network (CNN). Then, we apply a dual-domain attention module with a dimension reduction component to dynamically capture salient regions in both time and frequency domains. Finally, we use metric learning to optimize the fingerprint space, ensuring better separation between positive and negative samples. Experimental results show that our method achieves an AUC of 0.99 on ROC curves, outperforming typical methods by up to 5.3%, with particularly strong robustness under distortions including background noise, time scaling and sampling rate changes.

For the traditional model based on the deep learning method most used CNN(convolutional neural networks) or RNN(Recurrent neural Network) model and is based on the dynamic character-level embedding or word-level embedding as input, so there is a problem that the text feature extraction is not comprehensive. In the development environment of the Internet of Things, A method of Automatic text classification based on BERT(Bidirectional Encoder Representations from Transformers) and Feature Fusion was proposed in this paper. Firstly, the text-to-dynamic character-level embedding is transformed by the BERT model, and the BiLSTM(Bi-directional Long-Short Term Memory) and CNN output features are combined and merged to make full use of CNN to extract the advantages of local features and to use BiLSTM to have the advantage of memory to link the extracted context features to better represent the text, so as to improve the accuracy of text classification task. A comparative study with state-of-the-art approaches manifests the proposed method outperforms the state-of-the-art methods in accuracy. It can effectively improve the accuracy of tag prediction for text data with sequence features and obvious local features.