Abstract
This paper introduces a curated dataset of urban scenes for audio-visual scene analysis which consists of carefully selected and recorded material. The data was recorded in multiple European cities, using the same equipment, in multiple locations for each scene, and is openly available. We also present a case study for audio-visual scene recognition and show that joint modeling of audio and visual modalities brings significant performance gain compared to state of the art uni-modal systems. Our approach obtained an 84.8% accuracy compared to 75.8% for the audio-only and 68.4% for the video-only equivalent systems.
Original language | English |
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Title of host publication | ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Publisher | IEEE |
Pages | 626-630 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-7281-7605-5 |
DOIs | |
Publication status | Published - 2021 |
Publication type | A4 Article in conference proceedings |
Event | IEEE International Conference on Acoustics, Speech and Signal Processing - Metro Toronto Convention Centre, Toronto, Canada Duration: 6 Jun 2021 → 11 Jun 2021 https://2021.ieeeicassp.org |
Publication series
Name | Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing |
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ISSN (Print) | 1520-6149 |
Conference
Conference | IEEE International Conference on Acoustics, Speech and Signal Processing |
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Country/Territory | Canada |
City | Toronto |
Period | 6/06/21 → 11/06/21 |
Internet address |
Keywords
- Acoustic scene
- Audio-visual data
- Pattern recognition
- Scene analysis
- Transfer learning
Publication forum classification
- Publication forum level 1
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering