Abstract
Perch is a performant pre-trained model for bioacoustics. It was trained in supervised fashion, providing both off-the-shelf classification scores for thousands of vocalizing species as well as strong embeddings for transfer learning. In this new release, Perch 2.0, we expand from training exclusively on avian species to a large multi-taxa dataset. The model is trained with self-distillation using a prototype-learning classifier as well as a new source-prediction training criterion. Perch 2.0 obtains state-of-the-art performance on the BirdSet and BEANS benchmarks. It also outperforms specialized marine models on marine transfer learning tasks, despite having almost no marine training data. We present hypotheses as to why fine-grained species classification is a particularly robust pre-training task for bioacoustics.
Abstract (translated)
Perch 是一个高性能的预训练模型，专门用于生物声学领域。它采用监督学习方式训练而成，能够提供数千种发声物种的现成分类得分以及强大的迁移学习嵌入表示。在本次新发布的 Perch 2.0 中，我们从仅对鸟类物种进行专有训练扩展到了一个包含大量多分类群（multi-taxa）数据集的训练。Perch 2.0 使用自蒸馏技术和原型学习分类器，并引入了一种新的源预测训练准则来进一步优化模型性能。 在 BirdSet 和 BEANS 这两个基准测试中，Perch 2.0 达到了最先进的性能水平。尽管其几乎没有任何海洋数据的训练经验，但在海洋生物声学迁移学习任务上仍能超越专门针对海洋物种设计的预训练模型的表现。我们还提出了假设来解释为什么细粒度物种分类是一个特别稳健且有效的生物声学领域中的预训练任务。
URL
https://arxiv.org/abs/2508.04665
