SSL-Net：一种用于高效鸟类声音分类的协同频谱与学习网络
SSL-Net: A Synergistic Spectral and Learning-based Network for Efficient Bird Sound Classification
AI总结：
本研究提出SSL-Net框架，结合频谱与学习特征，实现高效准确的鸟类声音分类，在有限样本下表现优异，并提供了三种特征融合策略供选择。
摘要：
高效且准确的鸟类声音分类对于生态学、栖息地保护及科学研究具有重要意义，因为它在监测物种分布和丰度方面发挥着核心作用。然而，现有方法通常需要大量标注的音频数据集，并具有高度定制化的框架，从而带来了巨大的计算和标注负担。在本研究中，我们提出了一种名为SSL-Net的高效且通用的框架，该框架结合了频谱特征和学习特征来识别不同的鸟类声音。从标准野外采集的鸟类音频数据集中获得的令人鼓舞的实验结果验证了我们的方法在高效提取特征方面的有效性，并且即使在样本量有限的情况下，也能在鸟类声音分类中实现更高的性能。此外，我们提出了三种特征融合策略，通过定量分析帮助工程师和研究人员进行选择。
英文摘要：
Efficient and accurate bird sound classification is of important for ecology, habitat protection and scientific research, as it plays a central role in monitoring the distribution and abundance of species. However, prevailing methods typically demand extensively labeled audio datasets and have highly customized frameworks, imposing substantial computational and annotation loads. In this study, we present an efficient and general framework called SSL-Net, which combines spectral and learned features to identify different bird sounds. Encouraging empirical results gleaned from a standard field-collected bird audio dataset validate the efficacy of our method in extracting features efficiently and achieving heightened performance in bird sound classification, even when working with limited sample sizes. Furthermore, we present three feature fusion strategies, aiding engineers and researchers in their selection through quantitative analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
