{ "id": 490922, "title": "Papers on audio augmentation.", "url": "/competitions/birdclef-2024/discussion/490922", "author_name": "", "post_date": "2024-04-03T23:05:02.187932300Z", "votes": 15, "comment_count": 1, "views": 0, "content": "

I'm planning to try the audio augmentation methods from these papers in the competition.

\n", "messages": [ { "id": "2734070", "postDate": "04/03/2024 23:05:02", "content": "

I'm planning to try the audio augmentation methods from these papers in the competition.

\n", "rawMarkdown": "I'm planning to try the audio augmentation methods from these papers in the competition.\n\n- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://arxiv.org/abs/1904.08779)\ncode: https://github.com/DemisEom/SpecAugment\n\n- [Heavily Augmented Sound Event Detection utilizing Weak Predictions](https://arxiv.org/abs/2107.03649)\n- [FilterAugment: An Acoustic Environmental Data Augmentation Method](https://arxiv.org/abs/2110.03282)\ncode:https://github.com/frednam93/FilterAugSED\n\n- [SpecAugment++: A Hidden Space Data Augmentation Method for Acoustic Scene Classification](https://arxiv.org/abs/2103.16858)\ncode:https://github.com/WangHelin1997/SpecAugment-plus\n\n- [SpecMix : A Mixed Sample Data Augmentation method for Training withTime-Frequency Domain Features](https://arxiv.org/abs/2108.03020)\ncode:https://github.com/anas-rz/specmix-pytorch\n\n- [RandMasking Augment: A Simple and Randomized Data Augmentation for Acoustic Scene Classification](https://research.samsung.com/blog/RandMasking-Augment-A-Simple-and-Randomized-Data-Augmentation-for-Acoustic-Scene-Classification)", "votes": null }, { "id": "2734423", "postDate": "04/04/2024 05:47:16", "content": "

Picking up on the matter, there is this famous Audiomentations tool: https://github.com/iver56/audiomentations, with lots of different audio transforms, for those who intend to try other kinds of data augmentation.

", "rawMarkdown": "Picking up on the matter, there is this famous Audiomentations tool: https://github.com/iver56/audiomentations, with lots of different audio transforms, for those who intend to try other kinds of data augmentation.", "votes": null } ], "comments": [ { "id": 2734423, "author_name": "leonardoboulitreau", "author_url": "", "post_date": "04/04/2024 05:47:16", "content": "

Picking up on the matter, there is this famous Audiomentations tool: https://github.com/iver56/audiomentations, with lots of different audio transforms, for those who intend to try other kinds of data augmentation.

", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "2734070": "I'm planning to try the audio augmentation methods from these papers in the competition.\n\n- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://arxiv.org/abs/1904.08779)\ncode: https://github.com/DemisEom/SpecAugment\n\n- [Heavily Augmented Sound Event Detection utilizing Weak Predictions](https://arxiv.org/abs/2107.03649)\n- [FilterAugment: An Acoustic Environmental Data Augmentation Method](https://arxiv.org/abs/2110.03282)\ncode:https://github.com/frednam93/FilterAugSED\n\n- [SpecAugment++: A Hidden Space Data Augmentation Method for Acoustic Scene Classification](https://arxiv.org/abs/2103.16858)\ncode:https://github.com/WangHelin1997/SpecAugment-plus\n\n- [SpecMix : A Mixed Sample Data Augmentation method for Training withTime-Frequency Domain Features](https://arxiv.org/abs/2108.03020)\ncode:https://github.com/anas-rz/specmix-pytorch\n\n- [RandMasking Augment: A Simple and Randomized Data Augmentation for Acoustic Scene Classification](https://research.samsung.com/blog/RandMasking-Augment-A-Simple-and-Randomized-Data-Augmentation-for-Acoustic-Scene-Classification)", "2734423": "Picking up on the matter, there is this famous Audiomentations tool: https://github.com/iver56/audiomentations, with lots of different audio transforms, for those who intend to try other kinds of data augmentation." }, "source": "meta" }