Summary of Continual Audio-visual Sound Separation, by Weiguo Pian et al.
Continual Audio-Visual Sound Separation
by Weiguo Pian, Yiyang Nan, Shijian Deng, Shentong Mo, Yunhui Guo, Yapeng Tian
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Multimedia (cs.MM); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce a novel audio-visual sound separation task that allows models to continuously learn new sound sources while preserving performance on previously learned classes. The goal is to enhance the adaptability and robustness of audio-visual sound separation models for real-world scenarios where encountering new sound sources is common. To address these challenges, the authors propose a novel approach called ContAV-Sep, which includes a Cross-modal Similarity Distillation Constraint (CrossSDC) to mitigate catastrophic forgetting during continual learning. The authors demonstrate that ContAV-Sep can effectively mitigate catastrophic forgetting and achieve better performance compared to other continual learning baselines for audio-visual sound separation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for machines to separate sounds from different sources, like voices or music, using both hearing and seeing. Imagine being able to understand conversations in a noisy bar or pick out individual instruments in a concert hall just by listening and watching. The researchers developed a new method called ContAV-Sep that helps machines learn new sounds while remembering old ones, making it more useful for real-life situations. |
Keywords
» Artificial intelligence » Continual learning » Distillation
