Ballroom Dance Recognition from Audio Recordings

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Ballroom Dance Recognition from Audio Recordings

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Ballroom Dance Recognition from Audio Recordings

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2020 25th International Conference on Pattern Recognition (ICPR)

ISBN

978-1-7281-8808-9

ISSN

1051-4651

e-ISSN

1051-4651

Počet stran výsledku

8

Strana od-do

2142-2149

Název nakladatele

IEEE Computer Society

Místo vydání

Los Alamitos

Místo konání akce

Milan

Datum konání akce

10. 1. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000678409202033