Great collection of resources for this competition! Thanks!
", "rawMarkdown": "Great collection of resources for this competition! Thanks!", "votes": 1 }, { "id": 3150064, "postDate": "2025-03-15T03:10:06.287Z", "content": "Thank you, these resources would definitely help.
", "rawMarkdown": "Thank you, these resources would definitely help.", "votes": 2 }, { "id": 3200916, "postDate": "2025-05-13T08:20:37.463Z", "rawMarkdown": "", "isDeleted": true } ], "comments": [ { "id": 3203447, "author_name": "Diganta", "author_url": "", "post_date": "2025-05-16T18:37:35.087000", "content": "Great collection of resources for this competition! Thanks!
", "votes": 1, "replies": [] }, { "id": 3150064, "author_name": "Debarghya Mondal", "author_url": "", "post_date": "2025-03-15T03:10:06.287000", "content": "Thank you, these resources would definitely help.
", "votes": 2, "replies": [] }, { "id": 3200916, "author_name": "", "author_url": "", "post_date": "2025-05-13T08:20:37.463000", "content": "", "votes": 0, "replies": [] } ], "raw_markdown_by_id": { "3146757": "# BirdCLEF 2025: Comprehensive Resource Guide\n\n## Previous BirdCLEF Competitions\n- BirdCLEF 2024 - https://www.kaggle.com/competitions/birdclef-2024 - Focused on North American bird species identification\n- BirdCLEF 2023 - https://www.kaggle.com/competitions/birdclef-2023 - Bird sound identification in soundscapes\n- BirdCLEF 2022 - https://www.kaggle.com/competitions/birdclef-2022 - Identify Eastern African bird species by sound\n- BirdCLEF 2021 - https://www.kaggle.com/competitions/birdclef-2021 - Bird call identification\n- BirdCLEF 2020 - https://www.kaggle.com/competitions/birdsong-recognition - Bird song recognition\n\n## Related Acoustic Monitoring Competitions\n- Rainforest Connection Species Audio Detection - https://www.kaggle.com/competitions/rfcx-species-audio-detection - Detecting species in recordings from tropical forest soundscapes\n- Cornell Birdcall Identification - https://www.kaggle.com/competitions/cornell-birdcall-identification - Identifying bird species from continuous audio data\n- Freesound Audio Tagging - https://www.kaggle.com/competitions/freesound-audio-tagging-2019 - General-purpose audio tagging\n\n## Top Notebooks from Previous BirdCLEF Competitions\n\n### Data Exploration\n- BirdCLEF 2023 - EDA & Audio Visualization by Chris Deotte - https://www.kaggle.com/code/cdeotte/birdclef-2023-eda-audio-visualizations\n- BirdCLEF 2022 - Comprehensive Data Analysis by AWSAF - https://www.kaggle.com/code/awsaf49/birdclef-2022-comprehensive-data-analysis\n- Understanding Bird Sounds: Audio Processing by Muhammed Talo - https://www.kaggle.com/code/muhammedtalo/understanding-bird-sounds-audio-processing\n\n### Feature Extraction\n- BirdCLEF Baseline: Audio to Spectrogram Conversion by Philipp Singer - https://www.kaggle.com/code/philippsinger/audio-to-spectrograms-many-targets-one-model\n- Mel Spectrogram Feature Extraction by Kneroma - https://www.kaggle.com/code/kneroma/tacotron-2-mel-spectrogram-librosa-tf-tutorial\n- MFCC Feature Extraction for Audio by Haqishen - https://www.kaggle.com/code/haqishen/augmentation-methods-for-audio\n\n### Model Architectures\n- BirdCLEF 2023 1st Place Solution by Cailloux Team - https://www.kaggle.com/competitions/birdclef-2023/discussion/414819\n- BirdCLEF 2022 1st Place Solution by Henkel AI Team - https://www.kaggle.com/competitions/birdclef-2022/discussion/327108\n- CNN + Transformer for Bird Sound Classification by Hidehisa Arai - https://www.kaggle.com/code/hidehisaarai1213/introduction-to-sound-event-detection\n- Audio Classification with FastAI & TimeSformer by Vijayabhaskar J - https://www.kaggle.com/code/vbookshelf/birdclef-2023-fastai-and-timesformer-starter\n\n### Semi-Supervised & Limited Label Learning\n- Learning with Limited Labels - Pseudo-Labeling by Chris Deotte - https://www.kaggle.com/code/cdeotte/pseudo-labeling-qda-0-969\n- Self-Supervised Audio Feature Learning by Radek Osmulski - https://www.kaggle.com/code/radek1/self-supervised-learning-for-audio-classification\n- Data Augmentation Techniques for Audio by Debarshi Chanda - https://www.kaggle.com/code/debarshichanda/introduction-to-audio-mixup-augmentation\n\n## Relevant Scientific Papers\n\n### Bioacoustics & Sound Classification\n- Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021). BirdNET: A deep learning solution for avian diversity monitoring. Ecological Informatics, 61, 101236. https://doi.org/10.1016/j.ecoinf.2021.101236\n- Stowell, D., Wood, M. D., Pamuła, H., Stylianou, Y., & Glotin, H. (2019). Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge. Methods in Ecology and Evolution, 10(3), 368-380. https://doi.org/10.1111/2041-210X.13103\n- Sethi, S. S., Jones, N. S., Fulcher, B. D., Picinali, L., Clink, D. J., Klinck, H., ... & Ewers, R. M. (2020). Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set. Proceedings of the National Academy of Sciences, 117(29), 17049-17055. https://doi.org/10.1073/pnas.2004702117\n\n### Limited-Label Learning\n- Wei, C., Sohn, K., Mellina, C., Yuille, A., & Yang, F. (2021). Crest: A class-rebalancing self-training framework for imbalanced semi-supervised learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10857-10866. https://openaccess.thecvf.com/content/CVPR2021/papers/Wei_CREST_A_Class-REbalancing_Self-Training_Framework_for_Imbalanced_Semi-Supervised_Learning_CVPR_2021_paper.pdf\n- Sohn, K., Berthelot, D., Li, C. L., Zhang, Z., Carlini, N., Cubuk, E. D., ... & Raffel, C. (2020). FixMatch: Simplifying semi-supervised learning with consistency and confidence. Advances in neural information processing systems, 33, 596-608. https://proceedings.neurips.cc/paper/2020/file/06964dce9addb1c5cb5d6e3d9838f733-Paper.pdf\n- Wang, X., Liu, Z., & Yu, S. X. (2021). Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 12586-12595. https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Unsupervised_Feature_Learning_by_Cross-Level_Instance-Group_Discrimination_CVPR_2021_paper.pdf\n\n### Ecological Monitoring & Restoration\n- Burivalova, Z., Game, E. T., & Butler, R. A. (2019). The sound of a tropical forest. Science, 363(6422), 28-29. https://doi.org/10.1126/science.aav1902\n- Campos-Cerqueira, M., & Aide, T. M. (2016). Improving distribution data of threatened species by combining acoustic monitoring and occupancy modelling. Methods in Ecology and Evolution, 7(11), 1340-1348. https://doi.org/10.1111/2041-210X.12599\n- Aide, T. M., Corrada-Bravo, C., Campos-Cerqueira, M., Milan, C., Vega, G., & Alvarez, R. (2013). Real-time bioacoustics monitoring and automated species identification. PeerJ, 1, e103. https://doi.org/10.7717/peerj.103\n\n## Tools & Libraries\n\n### Audio Processing\n- Librosa - https://librosa.org/ - Python package for music and audio analysis\n- PyTorch Audio - https://pytorch.org/audio - Audio signal processing library\n- TensorFlow Audio - https://www.tensorflow.org/io/tutorials/audio - Audio processing tools in TensorFlow\n- AudioMoth - https://www.openacousticdevices.info/audiomoth - Low-cost acoustic monitoring device\n\n### Machine Learning Frameworks\n- FastAI Audio - https://docs.fast.ai/tutorial.audio.html - High-level API for audio tasks\n- Hugging Face Transformers - https://huggingface.co/docs/transformers/index - Transformers for audio classification\n- TimmAudio - https://github.com/rwightman/pytorch-image-models - Audio models based on Timm\n- PANNs - https://github.com/qiuqiangkong/audioset_tagging_cnn - Pre-trained audio neural networks\n\n### Data Visualization\n- AudioSet Ontology Explorer - https://research.google.com/audioset/ontology/index.html - Hierarchical structure of sound events\n- AudioSegment - https://github.com/jiaaro/pydub - Audio file manipulation\n\n## Colombian Biodiversity Resources\n- Humboldt Institute Biodiversity Database - http://www.humboldt.org.co/en/ - Colombian biodiversity information\n- Xeno-canto - https://www.xeno-canto.org/region/colombia - Colombia bird sound recordings\n- Red Ecoacústica Colombiana - https://redecoac.org/ - Colombian Ecoacoustic Network resources\n- Fundación Biodiversa Colombia - https://fundacionbiodiversa.org/ - Resources on El Silencio Natural Reserve\n\n## Additional Learning Resources\n- Cornell Lab of Ornithology Sound Analysis Resources - https://www.birds.cornell.edu/ccb/data-tools/ - Comprehensive tools for bioacoustics\n- Rainforest Connection Open Data - https://rfcx.org/open_data - Open access rainforest audio data\n- Bioacoustics Research Program - https://www.birds.cornell.edu/ccb/ - Academic resources from Cornell\n- DCASE Community - http://dcase.community/ - Detection and Classification of Acoustic Scenes and Events resources\n- AI for Earth Azure - https://www.microsoft.com/en-us/ai/ai-for-earth - Computing resources for conservation projects\n\n## Community Forums\n- r/MachineLearning Audio Classification Threads - https://www.reddit.com/r/MachineLearning/search?q=audio%20classification&restrict_sr=1\n- AI for Conservation Slack - https://wildlabs.net/community - Conservation tech community", "3203447": "Great collection of resources for this competition! Thanks!", "3150064": "Thank you, these resources would definitely help.", "3200916": "" } }