# Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detection

arXiv: https://arxiv.org/abs/2505.20956 · HTML: https://mlsanity.com/paper/2505.20956/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 13 of 13)

- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [Computational bioacoustics with deep learning: a review and roadmap](https://mlsanity.com/paper/2112.06725/) (2021)
- [Active Learning for Sound Event Detection](https://mlsanity.com/paper/2002.05033/) (2020)
- [animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics](https://mlsanity.com/paper/2406.01253/) (2024)
- [Few-shot bioacoustic event detection at the DCASE 2022 challenge](https://mlsanity.com/paper/2207.07911/) (2022)
- [Towards Deep Active Learning in Avian Bioacoustics](https://mlsanity.com/paper/2406.18621/) (2024)
- [Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection](https://mlsanity.com/paper/2309.08971/) (2023)
- [Understanding Uncertainty Sampling via Equivalent Loss](https://mlsanity.com/paper/2307.02719/) (2023)
- [Sample Noise Impact on Active Learning](https://mlsanity.com/paper/2109.01372/) (2021)
- [From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learning](https://mlsanity.com/paper/2403.08525/) (2024)
- [Robust sound event detection in bioacoustic sensor networks](https://mlsanity.com/paper/1905.08352/) (2019)
- [Margin-based sampling in high dimensions: When being active is less efficient than staying passive](https://mlsanity.com/paper/2212.00772/) (2022)
- [Deep Active Audio Feature Learning in Resource-Constrained Environments](https://mlsanity.com/paper/2308.13201/) (2023)

## Built on by (most cited first, 2 of 2)

- [Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning](https://mlsanity.com/paper/2607.13555/) (2026)
- [Cover First, Disagree Softly: Rethinking Mismatch-First Active Learning for Frame-Level Audio Classification](https://mlsanity.com/paper/2607.13571/) (2026)

## Abstract (arXiv)

Bioacoustic sound event detection (BioSED) is crucial for biodiversity conservation but faces practical challenges during model development and training: limited amounts of annotated data, sparse events, species diversity, and class imbalance. To address these challenges efficiently with a limited labeling budget, we apply the mismatch-first farthest-traversal (MFFT), an active learning method integrating committee voting disagreement and diversity analysis. We also refine an existing BioSED dataset specifically for evaluating active learning algorithms. Experimental results demonstrate that MFFT achieves a mAP of 68% when cold-starting and 71% when warm-starting (which is close to the fully-supervised mAP of 75%) while using only 2.3% of the annotations. Notably, MFFT excels in cold-start scenarios and with rare species, which are critical for monitoring endangered species, demonstrating its practical value.