Passive acoustic monitoring (PAM) in avian bioacoustics enables
cost-effective and extensive data collection with minimal disruption to natural
habitats. Despite advancements in computational avian bioacoustics, deep
learning models continue to encounter challenges in adapting to diverse
environments in practical PAM scenarios. This is primarily due to the scarcity
of annotations, which requires labor-intensive efforts from human experts.
Active learning (AL) reduces annotation cost and speed ups adaption to diverse
scenarios by querying the most informative instances for labeling. This paper
outlines a deep AL approach, introduces key challenges, and conducts a
small-scale pilot study.

被动声学监测（PAM）在鸟类生物声学中能够以最小干扰自然栖息地的方式实现经济高效、广泛的数据收集。本文概述了一种深度主动学习方法，介绍了关键挑战，并进行了一个小规模的试点研究。

面向鸟类生物声学的深度主动学习

Towards Deep Active Learning in Avian Bioacoustics

Deep learning (DL) models have emerged as a powerful tool in avian
bioacoustics to diagnose environmental health and biodiversity. However,
inconsistencies in research pose notable challenges hindering progress in this
domain. Reliable DL models need to analyze bird calls flexibly across various
species and environments to fully harness the potential of bioacoustics in a
cost-effective passive acoustic monitoring scenario. Data fragmentation and
opacity across studies complicate a comprehensive evaluation of general model
performance. To overcome these challenges, we present the BirdSet benchmark, a
unified framework consolidating research efforts with a holistic approach for
classifying bird vocalizations in avian bioacoustics. BirdSet harmonizes
open-source bird recordings into a curated dataset collection. This unified
approach provides an in-depth understanding of model performance and identifies
potential shortcomings across different tasks. By establishing baseline results
of current models, BirdSet aims to facilitate comparability, guide subsequent
data collection, and increase accessibility for newcomers to avian
bioacoustics.

深度学习模型在鸟类生物声学中作为强大的工具出现，但是研究中的不一致性对这一领域的进展构成了重大挑战。为了克服这些挑战，我们提出了 BirdSet 基准测试，这是一个统一的框架，通过整体方法来对鸟类生物声学中的鸟叫进行分类。BirdSet 通过将开源鸟类录音整合到一个经过精心策划的数据集合中，为当前模型的基线结果建立了统一的基础，旨在促进可比性、指导后续数据收集，并提高对鸟类生物声学的新手的可访问性。