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

Autor: Wang, Qing, Du, Jun, Zheng, Siyuan, Li, Yunqing, Wang, Yajian, Wu, Yuzhong, Hu, Hu, Yang, Chao-Han Huck, Siniscalchi, Sabato Marco, Wang, Yannan, Lee, Chin-Hui

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 embedd

Externí odkaz: http://arxiv.org/abs/2203.04114

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A Two-Stage Approach to Device-Robust Acoustic Scene Classification

Autor: Hu, Hu, Yang, Chao-Han Huck, Xia, Xianjun, Bai, Xue, Tang, Xin, Wang, Yajian, Niu, Shutong, Chai, Li, Li, Juanjuan, Zhu, Hongning, Bao, Feng, Zhao, Yuanjun, Siniscalchi, Sabato Marco, Wang, Yannan, Du, Jun, Lee, Chin-Hui

Publikováno v: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed. Our two-stage system lever

Externí odkaz: http://arxiv.org/abs/2011.01447

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Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation

In this technical report, we present a joint effort of four groups, namely GT, USTC, Tencent, and UKE, to tackle Task 1 - Acoustic Scene Classification (ASC) in the DCASE 2020 Challenge. Task 1 comprises two different sub-tasks: (i) Task 1a focuses o

Externí odkaz: http://arxiv.org/abs/2007.08389

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