See https://www.kaggle.com/c/freesound-audio-tagging-2019/
One year ago, Freesound and Google’s Machine Perception hosted an audio tagging competition challenging Kagglers to build a general-purpose auto tagging system. This year they’re back and taking the challenge to the next level with multi-label audio tagging, doubled number of audio categories, and a noisier than ever training set. If you like raising your ML game, this challenge is for you.
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Additional important informations:
- No pretrained models were allowed
- No external data was allowed
- Training can be made anywhere, but the inference had to be made in Kaggle kernels in less than one hour.
- Only few samples were manually labels, and a lot more were automatically labeled (and approximatively), therefore one of the challenges was to find
See https://www.kaggle.com/c/freesound-audio-tagging-2019/overview/evaluation
The primary competition metric will be label-weighted label-ranking average precision (lwlrap, pronounced "Lol wrap"). This measures the average precision of retrieving a ranked list of relevant labels for each test clip (i.e., the system ranks all the available labels, then the precisions of the ranked lists down to each true label are averaged)
I had the chance to team-up with three very talented people in this competiton, namely Maxim Shugaev, Lehan Yang, and Khoi Nguyen. It was a pleasure working with them and neither of us would have performed so well alone. We ended up 9th out of 880.
The competition took place from April, 4 2019 to June, 11 2019. I joined quite late (early May), and merged with my teammates about 3 weeks before the end.
Our solution is described in detail here : https://www.kaggle.com/c/freesound-audio-tagging-2019/discussion/95409#latest-564043
DCASE2019_Challenge_technical_report.pdf: Technical report submitted to the DCASE 2019 Challengemodeling-kernel.ipynb: Notebook that contains almost all our work, i.e. the training of different models. Also available on Kaggle
Data can be downloaded on the official Kaggle page : https://www.kaggle.com/c/freesound-audio-tagging-2019/data If you wish to rerun the notebook, the easiest way is to fork the Kaggle kernel.