[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Multimedia

arXiv:2203.04114 (cs)
[Submitted on 7 Mar 2022 (v1), last revised 1 Sep 2022 (this version, v3)]

Title:A study on joint modeling and data augmentation of multi-modalities for audio-visual scene classification

Authors:Qing Wang, Jun Du, Siyuan Zheng, Yunqing Li, Yajian Wang, Yuzhong Wu, Hu Hu, Chao-Han Huck Yang, Sabato Marco Siniscalchi, Yannan Wang, Chin-Hui Lee
View PDF
Abstract:In this paper, we propose two techniques, namely joint modeling and data augmentation, to improve system performances for audio-visual scene classification (AVSC). We employ pre-trained networks trained only on image data sets to extract video embedding; whereas for audio embedding models, we decide to train them from scratch. We explore different neural network architectures for joint modeling to effectively combine the video and audio modalities. Moreover, data augmentation strategies are investigated to increase audio-visual training set size. For the video modality the effectiveness of several operations in RandAugment is verified. An audio-video joint mixup scheme is proposed to further improve AVSC performances. Evaluated on the development set of TAU Urban Audio Visual Scenes 2021, our final system can achieve the best accuracy of 94.2% among all single AVSC systems submitted to DCASE 2021 Task 1b.
Comments: 5 pages, 1 figure
Subjects: Multimedia (cs.MM); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD); Audio and Speech Processing (eess.AS)
Cite as: arXiv:2203.04114 [cs.MM]
  (or arXiv:2203.04114v3 [cs.MM] for this version)
  https://doi.org/10.48550/arXiv.2203.04114

Submission history

From: Qing Wang [view email]
[v1] Mon, 7 Mar 2022 07:29:55 UTC (100 KB)
[v2] Thu, 31 Mar 2022 04:09:12 UTC (99 KB)
[v3] Thu, 1 Sep 2022 03:28:17 UTC (477 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
cs.MM
< prev   |   next >
new | recent | 2022-03
Change to browse by:
cs
cs.CV
cs.SD
eess
eess.AS

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)