Acoustic scene classification attempts to categorize audio signals into predetermined classes. Thus the problem setting is similar to speech recognition, except that the target classes are different and a lot more heterogeneous. Yet, there are several possible applications for acoustic event recognition; for example, your mobile phone might automatically detect that you are in a meeting and turn the device to silent mode automatically.
In this competition, the data consists of recordings of 15 different
contexts
(classes) such as
beach
,
home
,
restaurant
, etc. The recordings have been cut to 10 second long segments, of which the organizers have computed the
mel-spectra
as the input to your algorithm.
The organizers would like to thank the Audio Research Group of Tampere University of Technology (
[TUT](http://www.tut.fi/en/)
) for kindly providing access to the competition data. Parts of the data have been used for annual DCASE competitions; see e.g.,
[DCASE2017](http://www.cs.tut.fi/sgn/arg/dcase2017/index)
.
This competition is part of the TUT course
[SGN-41007 Pattern Recognition and Machine Learning](http://www.cs.tut.fi/courses/SGN-41007/)
.
Prizes
Each member of
the best TUT team
will receive a nerd T-shirt.
The three best TUT teams
will receive a certificate signed by the SGN lab leader.
Organizers
Heikki Huttunen, Associate Professor, Tampere University of Technology, Finland
Toni Heittola, Researcher, Tampere University of Technology, Finland
Antti Happonen, Researcher, Tampere University of Technology, Finland
References
Annamaria Mesaros, Toni Heittola, and Tuomas Virtanen. TUT database for acoustic scene classification and sound event detection. In 24th European Signal Processing Conference 2016 (EUSIPCO 2016), pp 1128-1132. Budapest, Hungary, Aug 2016.
Annamaria Mesaros, Toni Heittola, Aleksandr Diment, Benjamin Elizalde, Ankit Shah, Emmanuel Vincent, Bhiksha Raj, and Tuomas Virtanen. DCASE 2017 challenge setup: tasks, datasets and baseline system. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 85–92. November 2017.