ARIONet：一种用于鸟类鸣声分类与未来帧预测的高级自监督对比表示网络
ARIONet: An Advanced Self-supervised Contrastive Representation Network for Birdsong Classification and Future Frame Prediction
AI总结：
针对现有鸟类鸣声分类依赖标注且忽略时间动态的问题，提出自监督对比网络ARIONet，联合优化对比分类与未来帧预测，在四个数据集上取得最高98.41%准确率，验证了其有效性。
摘要：
自动鸟类鸣声分类对于推进生态监测和生物多样性研究至关重要。尽管近期取得了进展，现有方法往往严重依赖标注数据，使用有限的特征表示，并忽视了物种准确识别所必需的时间动态。在本工作中，我们提出了一种自监督对比网络ARIONet（声学表示帧间目标网络），该网络利用增强的音频表示联合优化对比分类和未来帧预测。该模型在基于Transformer的编码器架构内同时整合多种互补的音频特征。我们的框架设计有两个关键目标：（1）通过最大化同一音频片段增强视图之间的相似性同时推开不同样本，学习具有判别性的物种特定表示以用于对比学习；（2）通过预测未来音频帧来建模时间动态，两者均无需大规模标注。我们在四个多样化的鸟类鸣声数据集上验证了我们的框架，包括British Birdsong Dataset、Bird Song Dataset以及两个扩展的Xeno-Canto子集（A-M和N-Z）。我们的方法持续优于现有基线，分别实现了98.41%、93.07%、91.89%和91.58%的分类准确率，以及97.84%、94.10%、91.29%和90.94%的F1分数。此外，在未来帧预测任务中，该方法展示了低平均绝对误差和高余弦相似度（高达95%）。大量实验进一步证实了我们的自监督学习策略在捕获复杂声学模式和时间依赖性方面的有效性，以及其在生态保护与监测中的实际应用潜力。
英文摘要：
Automated birdsong classification is essential for advancing ecological monitoring and biodiversity studies. Despite recent progress, existing methods often depend heavily on labeled data, use limited feature representations, and overlook temporal dynamics essential for accurate species identification. In this work, we propose a self-supervised contrastive network, ARIONet (Acoustic Representation for Interframe Objective Network), that jointly optimizes contrastive classification and future frame prediction using augmented audio representations. The model simultaneously integrates multiple complementary audio features within a transformer-based encoder model. Our framework is designed with two key objectives: (1) to learn discriminative species-specific representations for contrastive learning through maximizing similarity between augmented views of the same audio segment while pushing apart different samples, and (2) to model temporal dynamics by predicting future audio frames, both without requiring large-scale annotations. We validate our framework on four diverse birdsong datasets, including the British Birdsong Dataset, Bird Song Dataset, and two extended Xeno-Canto subsets (A-M and N-Z). Our method consistently outperforms existing baselines and achieves classification accuracies of 98.41%, 93.07%, 91.89%, and 91.58%, and F1-scores of 97.84%, 94.10%, 91.29%, and 90.94%, respectively. Furthermore, it demonstrates low mean absolute errors and high cosine similarity, up to 95\%, in future frame prediction tasks. Extensive experiments further confirm the effectiveness of our self-supervised learning strategy in capturing complex acoustic patterns and temporal dependencies, as well as its potential for real-world applicability in ecological conservation and monitoring.
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