Bejarano Rodriguez, Ronal
Fagerlund, Eemi
Koskimies, Aino
Heittola, Toni
Heittola, Toni
Mesaros, Annamaria
Virtanen, Tuomas
2018-04-24
<p>TUT Urban Acoustic Scenes 2018 Mobile development dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p>
<ul>
<li>Airport - <em>airport</em></li>
<li>Indoor shopping mall - <em>shopping_mall</em></li>
<li>Metro station - <em>metro_station</em></li>
<li>Pedestrian street - <em>street_pedestrian</em></li>
<li>Public square - <em>public_square</em></li>
<li>Street with medium level of traffic - <em>street_traffic</em></li>
<li>Travelling by a tram - <em>tram</em></li>
<li>Travelling by a bus - <em>bus</em></li>
<li>Travelling by an underground metro - <em>metro</em></li>
<li>Urban park - <em>park</em></li>
</ul>
<p>Recordings were made with three devices that captured audio simultaneously. Each acoustic scene has 864 segments (144 minutes of audio) recorded with device A (main device) and 72 segments of parallel audio (12 minutes) each recorded with devices B and C. The dataset contains in total 28 hours of audio.</p>
https://doi.org/10.5281/zenodo.1228235
oai:zenodo.org:1228235
Zenodo
https://zenodo.org/communities/dcase
https://zenodo.org/communities/tut-arg
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.1228234
info:eu-repo/semantics/openAccess
Other (Non-Commercial)
computational auditory scene analysis
acoustic scene classification
audio
TUT Urban Acoustic Scenes 2018 Mobile, Development dataset
info:eu-repo/semantics/other