Hey Kagglers,
\nThe Cornell Birdsong Identification Competition has closed! We hope you were able to learn a lot, and use your machine learning skills on this audio classification challenge - now the largest audio competition in Kaggle history!
\nThis year’s competition ended with 1,630 participants on 1,390 teams! We had 16,628 submissions from 70 countries! For 203 users (including 15 in the top 100!), this was their first competition. We also had 2 new Grandmasters and 4 new Masters at the conclusion of the competition! Thank you all for your hard work in this competition and congratulations to our winners and to those who gained a new ranking!
\nWe were excited to work with the teams at Cornell and the Bioacoustics team at Google (Holger, Stefan,Tom) for this competition. We're glad they choose Kaggle to be a platform to bring these types of problems to the broader data science community. They've been a pleasure to work with, and we hope to work with them again in the future.
\nWe're also continually pleased to see how Kaggle Competitions serve as a great medium and vehicle through which the greater data science community can learn and develop their machine learning abilities and skills. Beginners and experts can come together to start, grow, and succeed. Further, in this competition, many can use their machine learning skills to help a great cause like this one.
\nWe've cleaned the leaderboard and disqualified some teams that have violated the rules. If you think you were removed by mistake, or believe you have evidence that suggests another team cheated, please contact compliance. Please fill in all the fields honestly.
\nThe top potential winning teams have been contacted via email to provide their winning solutions for host review. Should we need to move down the leaderboard for any reason, we will do so and continue to make contact with subsequent teams.
\nYou should have already seen your points and medals awarded, however it may take a few hours to propagate through if yours are still missing.
In the meantime, I've created this thread to be a curated list of your work. As you post your solutions, top kernels, and general discussion posts, I'll update this thread to keep track. If you write a paper, thesis, or present at a conference, and would like to share your work, please let us know so we can share with the Kaggle community! Additionally, if you performed any data manipulation, or used a new technique in this competition that you'd like to share to further the industry, please let us know and we'll post it here! That way, should you be looking for takeaways in the future, or if you've just now stumbled across the competition, you have a place to see what surfaced from the work performed.
\nHappy Modeling!
\nKaggle Team
Hey Kagglers,
\nThe Cornell Birdsong Identification Competition has closed! We hope you were able to learn a lot, and use your machine learning skills on this audio classification challenge - now the largest audio competition in Kaggle history!
\nThis year’s competition ended with 1,630 participants on 1,390 teams! We had 16,628 submissions from 70 countries! For 203 users (including 15 in the top 100!), this was their first competition. We also had 2 new Grandmasters and 4 new Masters at the conclusion of the competition! Thank you all for your hard work in this competition and congratulations to our winners and to those who gained a new ranking!
\nWe were excited to work with the teams at Cornell and the Bioacoustics team at Google (Holger, Stefan,Tom) for this competition. We're glad they choose Kaggle to be a platform to bring these types of problems to the broader data science community. They've been a pleasure to work with, and we hope to work with them again in the future.
\nWe're also continually pleased to see how Kaggle Competitions serve as a great medium and vehicle through which the greater data science community can learn and develop their machine learning abilities and skills. Beginners and experts can come together to start, grow, and succeed. Further, in this competition, many can use their machine learning skills to help a great cause like this one.
\nWe've cleaned the leaderboard and disqualified some teams that have violated the rules. If you think you were removed by mistake, or believe you have evidence that suggests another team cheated, please contact compliance. Please fill in all the fields honestly.
\nThe top potential winning teams have been contacted via email to provide their winning solutions for host review. Should we need to move down the leaderboard for any reason, we will do so and continue to make contact with subsequent teams.
\nYou should have already seen your points and medals awarded, however it may take a few hours to propagate through if yours are still missing.
In the meantime, I've created this thread to be a curated list of your work. As you post your solutions, top kernels, and general discussion posts, I'll update this thread to keep track. If you write a paper, thesis, or present at a conference, and would like to share your work, please let us know so we can share with the Kaggle community! Additionally, if you performed any data manipulation, or used a new technique in this competition that you'd like to share to further the industry, please let us know and we'll post it here! That way, should you be looking for takeaways in the future, or if you've just now stumbled across the competition, you have a place to see what surfaced from the work performed.
\nHappy Modeling!
\nKaggle Team
Huge gratitude to all of the participants! We are currently reviewing all of the fantastic write-ups. In addition to the great insights from experienced participants, it's been wonderful to see so many people taking their first attempts at ML on Audio, and all of the support amongst the community.
", "rawMarkdown": "Huge gratitude to all of the participants! We are currently reviewing all of the fantastic write-ups. In addition to the great insights from experienced participants, it's been wonderful to see so many people taking their first attempts at ML on Audio, and all of the support amongst the community.", "votes": null }, { "id": "1020036", "postDate": "09/20/2020 20:35:25", "content": "thank you for sharing this
", "rawMarkdown": "thank you for sharing this", "votes": null }, { "id": "1020675", "postDate": "09/21/2020 10:50:54", "content": "Congratulations!! to the winners.
", "rawMarkdown": "Congratulations!! to the winners.", "votes": null }, { "id": "1044536", "postDate": "10/09/2020 21:21:19", "content": "Thank you for sharing 👍
", "rawMarkdown": "Thank you for sharing 👍", "votes": null } ], "comments": [ { "id": 1016729, "author_name": "tomdenton", "author_url": "", "post_date": "09/19/2020 07:47:27", "content": "Huge gratitude to all of the participants! We are currently reviewing all of the fantastic write-ups. In addition to the great insights from experienced participants, it's been wonderful to see so many people taking their first attempts at ML on Audio, and all of the support amongst the community.
", "votes": null, "replies": [] }, { "id": 1044536, "author_name": "karlbina", "author_url": "", "post_date": "10/09/2020 21:21:19", "content": "Thank you for sharing 👍
", "votes": null, "replies": [] }, { "id": 1020036, "author_name": "berkaycihan", "author_url": "", "post_date": "09/20/2020 20:35:25", "content": "thank you for sharing this
", "votes": null, "replies": [] }, { "id": 1020675, "author_name": "tyadav", "author_url": "", "post_date": "09/21/2020 10:50:54", "content": "Congratulations!! to the winners.
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "1016357": "Hey Kagglers,\n\nThe Cornell Birdsong Identification Competition has closed! We hope you were able to learn a lot, and use your machine learning skills on this audio classification challenge - now the largest audio competition in Kaggle history!\n\nThis year’s competition ended with 1,630 participants on 1,390 teams! We had 16,628 submissions from 70 countries! For 203 users (including 15 in the top 100!), this was their first competition. We also had 2 new Grandmasters and 4 new Masters at the conclusion of the competition! Thank you all for your hard work in this competition and congratulations to our winners and to those who gained a new ranking! \n\nWe were excited to work with the teams at Cornell and the Bioacoustics team at Google ([Holger](https://www.kaggle.com/holgerklinck), [Stefan](https://www.kaggle.com/stefankahl),[Tom](https://www.kaggle.com/tomdenton)) for this competition. We're glad they choose Kaggle to be a platform to bring these types of problems to the broader data science community. They've been a pleasure to work with, and we hope to work with them again in the future. \n\nWe're also continually pleased to see how Kaggle Competitions serve as a great medium and vehicle through which the greater data science community can learn and develop their machine learning abilities and skills. Beginners and experts can come together to start, grow, and succeed. Further, in this competition, many can use their machine learning skills to help a great cause like this one.\n\nWe've cleaned the leaderboard and disqualified some teams that have violated the rules. If you think you were removed by mistake, or believe you have evidence that suggests another team cheated, please contact [compliance](https://www.kaggle.com/compliance). Please fill in all the fields honestly.\n\nThe top potential winning teams have been contacted via email to provide their winning solutions for host review. Should we need to move down the leaderboard for any reason, we will do so and continue to make contact with subsequent teams.\nYou should have already seen your points and medals awarded, however it may take a few hours to propagate through if yours are still missing.\n\nIn the meantime, I've created this thread to be a curated list of your work. As you post your solutions, top kernels, and general discussion posts, I'll update this thread to keep track. If you write a paper, thesis, or present at a conference, and would like to share your work, please let us know so we can share with the Kaggle community! Additionally, if you performed any data manipulation, or used a new technique in this competition that you'd like to share to further the industry, please let us know and we'll post it here! That way, should you be looking for takeaways in the future, or if you've just now stumbled across the competition, you have a place to see what surfaced from the work performed.\n\nHappy Modeling!\nKaggle Team\n\n###Top Solution Writeups\n- [1st Place Solution](https://www.kaggle.com/c/birdsong-recognition/discussion/183208)\n- [2nd Place Solution](https://www.kaggle.com/c/birdsong-recognition/discussion/183269)\n- [3rd Place Solution](https://www.kaggle.com/c/birdsong-recognition/discussion/183199)\n- [4th Place Solution](https://www.kaggle.com/c/birdsong-recognition/discussion/183339)\n- [5th Place Solution](https://www.kaggle.com/c/birdsong-recognition/discussion/183300)\n- [6th Place Solution](https://www.kaggle.com/c/birdsong-recognition/discussion/183204)\n- [8th Place Solution](https://www.kaggle.com/c/birdsong-recognition/discussion/183223)\n- [9th Place Solution](https://www.kaggle.com/c/birdsong-recognition/discussion/183315)\n- [10th Place Solution](https://www.kaggle.com/c/birdsong-recognition/discussion/183407)\n- [13th Place Solution](https://www.kaggle.com/c/birdsong-recognition/discussion/183436)\n- [18th Place Solution](https://www.kaggle.com/c/birdsong-recognition/discussion/183219)\n- [21st Place Solution](https://www.kaggle.com/c/birdsong-recognition/discussion/183209)\n- [36th Place Solution](https://www.kaggle.com/c/birdsong-recognition/discussion/183222)\n- [39th Place Solution](https://www.kaggle.com/c/birdsong-recognition/discussion/183258)\n- [43rd Place Solution](https://www.kaggle.com/c/birdsong-recognition/discussion/183240)\n\n###Top Notebooks\n- [Birdcall Recognition EDA and FE](https://www.kaggle.com/andradaolteanu/birdcall-recognition-eda-and-audio-fe)\n- [Introduction to Sound Event Detection](https://www.kaggle.com/hidehisaarai1213/introduction-to-sound-event-detection)\n- [Inference PyTorch ResNet Baseline](https://www.kaggle.com/hidehisaarai1213/inference-pytorch-birdcall-resnet-baseline)\n- [Training Birdsong Baseline ResNet50](https://www.kaggle.com/ttahara/training-birdsong-baseline-resnest50-fast)\n- [EDA and Audio Processing with Python](https://www.kaggle.com/parulpandey/eda-and-audio-processing-with-python)\n- [Comprehensive Feature Engineering Tutorial](https://www.kaggle.com/pavansanagapati/comprehensive-feature-engineering-tutorial)\n\n###Other Top Discussions\n- [Previous Works for Inspirations](https://www.kaggle.com/c/birdsong-recognition/discussion/158933)\n- [Resampled Train Audio on Kaggle Dataset](https://www.kaggle.com/c/birdsong-recognition/discussion/164197)\n- [Previous Audio Competitions and Solutions](https://www.kaggle.com/c/birdsong-recognition/discussion/158943)\n- [Early Competition Starter Pack](https://admin.kaggle.com/c/birdsong-recognition/discussion/160222)", "1016729": "Huge gratitude to all of the participants! We are currently reviewing all of the fantastic write-ups. In addition to the great insights from experienced participants, it's been wonderful to see so many people taking their first attempts at ML on Audio, and all of the support amongst the community.", "1020036": "thank you for sharing this", "1020675": "Congratulations!! to the winners.", "1044536": "Thank you for sharing 👍" }, "source": "meta" }