Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy

• URL: http://arxiv.org/abs/2204.11420v1
• Date: Mon, 25 Apr 2022 03:37:02 GMT
• Title: Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy
• Authors: Chengxin Chen, Meng Wang, Pengyuan Zhang
• Abstract summary: We propose a joint training framework, using the acoustic features and raw images directly as inputs for the AVSC task. Specifically, we retrieve the bottom layers of pre-trained image models as visual encoder, and jointly optimize the scene classifier and 1D-CNN based acoustic encoder during training.
• Score: 26.975596225131824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, audio-visual scene classification (AVSC) has attracted increasing attention from multidisciplinary communities. Previous studies tended to adopt a pipeline training strategy, which uses well-trained visual and acoustic encoders to extract high-level representations (embeddings) first, then utilizes them to train the audio-visual classifier. In this way, the extracted embeddings are well suited for uni-modal classifiers, but not necessarily suited for multi-modal ones. In this paper, we propose a joint training framework, using the acoustic features and raw images directly as inputs for the AVSC task. Specifically, we retrieve the bottom layers of pre-trained image models as visual encoder, and jointly optimize the scene classifier and 1D-CNN based acoustic encoder during training. We evaluate the approach on the development dataset of TAU Urban Audio-Visual Scenes 2021. The experimental results show that our proposed approach achieves significant improvement over the conventional pipeline training strategy. Moreover, our best single system outperforms previous state-of-the-art methods, yielding a log loss of 0.1517 and accuracy of 94.59% on the official test fold.

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