Bibliography

All sources cited in this documentation, sorted alphabetically by first author. The numbers are the same on every page, and each page also lists its sources at the bottom.

[1] Allen, J. B., & Rabiner, L. R. (1977). "A unified approach to short-time Fourier analysis and synthesis." Proceedings of the IEEE, 65(11), 1558–1564.

Cited in: How It Works › Algorithm Overview

[2] Ayers, J., Perry, S., Prestrelski, S., et al. (2024). "A deep learning approach to the automated segmentation of bird vocalizations from weakly labeled crowd-sourced audio." NeurIPS 2024 Workshop on Tackling Climate Change with Machine Learning.

Cited in: Models and Metrics › Just-Bird › Label Refinement

[3] Furlanello, T., Lipton, Z. C., Tschannen, M., Itti, L., & Anandkumar, A. (2018). "Born again neural networks." Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80:1607–1616.

Cited in: Models and Metrics › Just-Bird › Training

[4] Gulrajani, I., & Lopez-Paz, D. (2021). "In search of lost domain generalization." International Conference on Learning Representations (ICLR).

Cited in: Models and Metrics › Just-Bird › Training

[5] Harris, F. J. (1978). "On the use of windows for harmonic analysis with the discrete Fourier transform." Proceedings of the IEEE, 66(1):51–83.

Cited in: How It Works › Spectrogram Generation

[6] Heinzel, G., Rüdiger, A., & Schilling, R. (2002). "Spectrum and spectral density estimation by the Discrete Fourier transform (DFT), including a comprehensive list of window functions and some new flat-top windows." Technical report, Max-Planck-Institut für Gravitationsphysik (Albert-Einstein-Institut), Hannover.

Cited in: How It Works › Spectrogram Generation

[7] Hinton, G., Vinyals, O., & Dean, J. (2015). "Distilling the knowledge in a neural network." arXiv preprint arXiv:1503.02531.

Cited in: Models and Metrics › Just-Bird › Training

[8] Hosang, J., Benenson, R., & Schiele, B. (2017). "Learning non-maximum suppression." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4507–4515.

Cited in: How It Works › Algorithm Overview

[9] Jocher, G., & Qiu, J. (2024). Ultralytics YOLO11 (Version 11.0.0) [Computer software]. AGPL-3.0. https://github.com/ultralytics/ultralytics. There is no YOLO11 paper; this is the citation given in the YOLO11 documentation.

Cited in: How It Works › Algorithm Overview

[10] Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021). "BirdNET: A deep learning solution for avian diversity monitoring." Ecological Informatics, 61:101236.

Cited in: How It Works › Algorithm Overview

[11] Koh, P. W., Sagawa, S., Marklund, H., et al. (2021). "WILDS: A benchmark of in-the-wild distribution shifts." Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139:5637–5664.

Cited in: Models and Metrics › Just-Bird › Model and Performance

[12] Kuhn, H. W. (1955). "The Hungarian method for the assignment problem." Naval Research Logistics Quarterly, 2(1–2):83–97.

Cited in: CLI Reference › F-Beta Score Analysis, CLI Reference › Confusion Matrix Analysis, API Reference › FBetaScoreAnalyzer, API Reference › ConfusionMatrixAnalyzer

[13] Lee, D.-H. (2013). "Pseudo-Label: The simple and efficient semi-supervised learning method for deep neural networks." ICML 2013 Workshop on Challenges in Representation Learning.

Cited in: Models and Metrics › Just-Bird › Training

[14] Lin, T.-Y., Maire, M., Belongie, S., et al. (2014). "Microsoft COCO: Common objects in context." Computer Vision – ECCV 2014, 740–755.

Cited in: Models and Metrics › Just-Bird › Model and Performance

[15] Liu, Y.-C., Ma, C.-Y., He, Z., Kuo, C.-W., Chen, K., Zhang, P., Wu, B., Kira, Z., & Vajda, P. (2021). "Unbiased teacher for semi-supervised object detection." International Conference on Learning Representations (ICLR).

Cited in: Models and Metrics › Just-Bird › Label Refinement

[16] Lostanlen, V., Salamon, J., Cartwright, M., McFee, B., Farnsworth, A., Kelling, S., & Bello, J. P. (2019). "Per-channel energy normalization: Why and how." IEEE Signal Processing Letters, 26(1):39–43.

Cited in: How It Works › Spectrogram Generation

[17] Lostanlen, V., Salamon, J., Farnsworth, A., Kelling, S., & Bello, J. P. (2019). "Robust sound event detection in bioacoustic sensor networks." PLOS ONE, 14(10):e0214168.

Cited in: How It Works › Spectrogram Generation

[18] Lostanlen, V. (2021). "Self-calibrating acoustic sensor networks with per-channel energy normalization." Proceedings of Euronoise 2021. Sociedade Portuguesa de Acústica.

Cited in: How It Works › Algorithm Overview

[19] MacPhail, A. G., Yip, D. A., Knight, E. C., Hedley, R., Knaggs, M., Shonfield, J., Upham-Mills, E., & Bayne, E. M. (2024). "Audio data compression affects acoustic indices and reduces detections of birds by human listening and automated recognisers." Bioacoustics, 33(1):74–90.

Cited in: How It Works › Troubleshooting, Data In/Out › In: Audio and Model, CLI Reference › Detecting Birds, API Reference › BirdCallDetector

[20] McFee, B., et al. (2015). "librosa: Audio and Music Signal Analysis in Python." Proceedings of the 14th Python in Science Conference (SciPy).

Cited in: How It Works › Spectrogram Generation

[21] Mesaros, A., Heittola, T., & Virtanen, T. (2016). "Metrics for polyphonic sound event detection." Applied Sciences, 6(6):162.

Cited in: CLI Reference › F-Beta Score Analysis

[22] Powers, D. M. (2020). "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation." arXiv preprint arXiv:2010.16061.

Cited in: CLI Reference › F-Beta Score Analysis, API Reference › FBetaScoreAnalyzer

[23] Redmon, J., et al. (2016). "You Only Look Once: Unified, Real-Time Object Detection.“ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Cited in: How It Works › Algorithm Overview

[24] van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworths, London.

Cited in: CLI Reference › F-Beta Score Analysis, API Reference › FBetaScoreAnalyzer

[25] Roberts, D. R., et al. (2017). "Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure." Ecography, 40(8):913–929.

Cited in: Models and Metrics › Overview

[26] Sohn, K., Zhang, Z., Li, C.-L., Zhang, H., Lee, C.-Y., & Pfister, T. (2020). "A simple semi-supervised learning framework for object detection." arXiv preprint arXiv:2005.04757.

Cited in: Models and Metrics › Just-Bird › Label Refinement

[27] Solovyev, R., Wang, W., & Gabruseva, T. (2021). "Weighted boxes fusion: Ensembling boxes from different object detection models." Image and Vision Computing, 104117.

Cited in: How It Works › Algorithm Overview

[28] Song, H., Kim, M., Park, D., Shin, Y., & Lee, J.-G. (2023). "Learning from noisy labels with deep neural networks: A survey." IEEE Transactions on Neural Networks and Learning Systems, 34(11):8135–8153.

Cited in: Models and Metrics › Just-Bird › Label Refinement

[29] Wang, Y., Getreuer, P., Hughes, T., Lyon, R. F., & Saurous, R. A. (2017). "Trainable frontend for robust and far-field keyword spotting." Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5670–5674.

Cited in: How It Works › Spectrogram Generation

[30] Xie, Q., Luong, M.-T., Hovy, E., & Le, Q. V. (2020). "Self-training with Noisy Student improves ImageNet classification." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR42600.2020.01070

Cited in: Models and Metrics › Just-Bird › Training

[31] Zhang, H., Cisse, M., Dauphin, Y. N., & Lopez-Paz, D. (2018). "mixup: Beyond empirical risk minimization." International Conference on Learning Representations (ICLR).

Cited in: Models and Metrics › Just-Bird › Model and Performance