跨越物种界限：从语音到动物声音的迁移学习
Crossing the Species Divide: Transfer Learning from Speech to Animal Sounds
AI总结：
本文研究了自监督语音模型在生物声学检测中的迁移学习能力，展示了HuBERT、WavLM和XEUS等模型在动物声音识别中的有效性，并探讨了噪声和频率范围对性能的影响。
摘要：
自监督语音模型在语音处理中表现出色，但其在非语音数据上的有效性仍待探索。我们研究了此类模型在生物声学检测和分类任务中的迁移学习能力。我们证明了诸如HuBERT、WavLM和XEUS等模型能够生成跨物种的动物声音丰富潜在表示。我们通过线性探测时间平均表示分析模型属性。然后，我们将方法扩展到考虑时间信息的影响，使用其他下游架构。最后，我们研究了频率范围和噪声对性能的影响。值得注意的是，我们的结果与微调的生物声学预训练模型相当，并展示了噪声鲁棒预训练设置的影响。这些发现突显了基于语音的自监督学习作为推进生物声学研究的有效框架的潜力。
英文摘要：
Self-supervised speech models have demonstrated impressive performance in speech processing, but their effectiveness on non-speech data remains underexplored. We study the transfer learning capabilities of such models on bioacoustic detection and classification tasks. We show that models such as HuBERT, WavLM, and XEUS can generate rich latent representations of animal sounds across taxa. We analyze the models properties with linear probing on time-averaged representations. We then extend the approach to account for the effect of time-wise information with other downstream architectures. Finally, we study the implication of frequency range and noise on performance. Notably, our results are competitive with fine-tuned bioacoustic pre-trained models and show the impact of noise-robust pre-training setups. These findings highlight the potential of speech-based self-supervised learning as an efficient framework for advancing bioacoustic research.
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。
