Ballroom Dance Recognition from Audio Recordings
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00345502" target="_blank" >RIV/68407700:21230/21:00345502 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICPR48806.2021.9412255" target="_blank" >https://doi.org/10.1109/ICPR48806.2021.9412255</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICPR48806.2021.9412255" target="_blank" >10.1109/ICPR48806.2021.9412255</a>
Alternative languages
Result language
angličtina
Original language name
Ballroom Dance Recognition from Audio Recordings
Original language description
We propose a CNN-based approach to classify ten genres of ballroom dances given audio recordings, five latin and five standard, namely Cha Cha Cha, Jive, Paso Doble, Rumba, Samba, Quickstep, Slow Foxtrot, SlowWaltz, Tango and Viennese Waltz. We utilize a spectrogram of an audio signal and we treat it as an image that is an input of the CNN. The classification is performed independently by 5-seconds spectrogram segments in sliding window fashion and the results are then aggregated. The method was tested on following datasets: Publicly available Extended Ballroom dataset collected by Marchand and Peeters, 2016 and two YouTube datasets collected by us, one in studio quality and the other, more challenging, recorded on mobile phones. The method achieved accuracy 93.9%, 96.7% and 89.8% respectively. The method runs in real-time. We implemented a web application to demonstrate the proposed method.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 25th International Conference on Pattern Recognition (ICPR)
ISBN
978-1-7281-8808-9
ISSN
1051-4651
e-ISSN
1051-4651
Number of pages
8
Pages from-to
2142-2149
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Milan
Event date
Jan 10, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000678409202033