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.
![Freesound][1]
Here's the background: Some sounds are distinct and instantly recognizable, like a baby’s laugh or the strum of a guitar. Other sounds are difficult to pinpoint. If you close your eyes, could you tell the difference between the sound of a chainsaw and the sound of a blender?
Because of the vastness of sounds we experience, no reliable automatic general-purpose audio tagging systems exist. A significant amount of manual effort goes into tasks like annotating sound collections and providing captions for non-speech events in audiovisual content.
To tackle this problem,
[Freesound](https://freesound.org/)
(an initiative by
[MTG-UPF](https://www.upf.edu/web/mtg)
that maintains a collaborative database with over 400,000 Creative Commons Licensed sounds) and
[Google Research’s Machine Perception Team](https://research.google.com/teams/perception/)
(creators of
[AudioSet](https://research.google.com/audioset/)
, a large-scale dataset of manually annotated audio events with over 500 classes) have teamed up to develop the dataset for this new competition.
To win this competition, Kagglers will develop an algorithm to tag audio data automatically using a diverse vocabulary of 80 categories.
If successful, your systems could be used for several applications, ranging from automatic labelling of sound collections to the development of systems that automatically tag video content or recognize sound events happening in real time.
Ready to raise your game? Join the competition!
Note, this competition is similar in nature to
[this competition](https://www.kaggle.com/c/freesound-audio-tagging)
with a new dataset, and multi-class labels.
## Organizers

 -
[Eduardo Fonseca](http://www.eduardofonseca.net/)
,
[MTG-UPF](https://www.upf.edu/web/mtg)
, Barcelona
 -
[Manoj Plakal](https://ai.google/research/people/author8115)
,
[Google's Sound Understanding](https://research.google.com/audioset////////about.html)
, New York
 -
[Frederic Font](https://ffont.github.io/)
,
[MTG-UPF](https://www.upf.edu/web/mtg)
, Barcelona
 -
[Dan Ellis](https://ai.google/research/people/DanEllis)
,
[Google's Sound Understanding](https://research.google.com/audioset////////about.html)
, New York

>**This is a Kernels-only competition. Refer to
[Kernels Requirements](https://www.kaggle.com/c/freesound-audio-tagging-2019/overview/kernels-requirements)
for details.**


  [1]: https://storage.googleapis.com/kaggle-media/competitions/freesound/task2_freesound_audio_tagging.png