Task description
This task is part of the BioDCASE Challenge 2026 and focuses on estimating bird abundance from acoustic recordings collected in zoo aviaries. The goal is to develop robust methods for counting individuals of a target species in realistic, multi-species acoustic environments where ground-truth population sizes are known. The main leaderboard ranks systems based solely on target-species abundance estimation performance.
Awards
The official winning submission is determined by the official evaluation ranking. The Jury Award is selected by the task organisers based on the submitted systems and technical reports.
Official winning submission
Jury award
Submission ranking
The official submission ranking is based on the average MAE across each of the test sets. Individual performance on each test set is reported below.
| Submission information | Rank | Test set | Dev set | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Submission | System name | Technical report |
Official rank |
Rank score |
MAE Flamingo | MAE Quelea | MAE Ibis | RMSE | R2 | MAPE | Avg MAE (dev) |
| Yang_WHU_task6_1 | Two-Stage CRNN Regression Framework | yangCRNN2026 | 1 | 9.17 | 20.00 | 4.50 | 3.00 | 13.04 | 0.97 | 71.68 | 0.75 |
| Leon_UFAM_task6_3 | ARIA fCWR x Stochastic Band Ratio (linear) | leonMorphology2026 | 2 | 13.33 | 23.50 | 6.00 | 10.50 | 18.02 | 0.94 | 103.98 | 2.25 |
| You_PKU_task6_2 | Low-Parameter Detector Ensemble (stacked flamingo) | youDetector2026 | 3 | 22.67 | 61.50 | 3.50 | 3.00 | 48.69 | 0.59 | 207.23 | 0.00 |
| official_aria_baseline | Official ARIA Baseline (flock_corrected_cwr) | arginBaseline2026 | 4 | 24.50 | 69.00 | 1.50 | 3.00 | 51.35 | 0.55 | 56.20 | 11.50 |
| Zhong_NJU_task6_1 | Validation-Gated Sampling-Based ARIA Counting | zhongARIA2026 | 5 | 46.67 | 83.00 | 40.50 | 16.50 | 58.99 | 0.40 | 234.95 | 11.50 |
Technical reports
Acoustic Bird Abundance Estimation Using Morphological Features of the Spectrogram
Leon Rojas, Yuneva E. and Frery, Alejandro C. and Colonna, Juan G.
Universidade Federal do Amazonas; Victoria University of Wellington
Source code: https://github.com/Yunevda/biodcase2026-population-estimation
Acoustic Bird Abundance Estimation Using Morphological Features of the Spectrogram
Leon Rojas, Yuneva E. and Frery, Alejandro C. and Colonna, Juan G.
Universidade Federal do Amazonas; Victoria University of Wellington
Method summary
| Features | flock-corrected CWR; band-filtered stochastic ratio (mathematical morphology) |
| Decision rule | least-squares linear regression on fCWR x stochastic band ratio |
Code
Source codeA Two-Stage CRNN Regression Framework for Bird Counting
Yang, Jing and Li, Siyi and Zheng, Zihan and Zhang, Heng and Ni, Youran and Wang, Yuzhu and Huang, Gongping
Wuhan University; Beijing Jiaotong University; Tampere University
A Two-Stage CRNN Regression Framework for Bird Counting
Yang, Jing and Li, Siyi and Zheng, Zihan and Zhang, Heng and Ni, Youran and Wang, Yuzhu and Huang, Gongping
Wuhan University; Beijing Jiaotong University; Tampere University
Method summary
| Features | log-mel spectrogram (128-band) |
| Training or augmentation | SpecAugment; synthetic data mixing |
| Decision rule | fragment-level CRNN regression + aviary-level linear aggregation |
Low-Parameter Detector Ensembles for Bird Counting
You, Yuhuan
Peking University
No source code submitted.
Low-Parameter Detector Ensembles for Bird Counting
You, Yuhuan
Peking University
Method summary
| Features | detection rate; confidence-weighted rate; bout statistics; MAAD acoustic indices |
| Embeddings | BirdNET; ARIA |
| Decision rule | species-specific low-parameter regression |
Validation-Gated Sampling-Based ARIA Counting System
Zhong, Chongyang
Nanjing University
Code included in submission package.
Validation-Gated Sampling-Based ARIA Counting System
Zhong, Chongyang
Nanjing University
Method summary
| Features | confidence-weighted detection rate |
| Decision rule | stratified sampling + linear calibration + species-wise validation gating |