Enhancing Real-World Active Speaker Detection with Multi-Modal Extraction Pre-Training
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911821146882048 |
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| author | Tao, Ruijie Qian, Xinyuan Das, Rohan Kumar Gao, Xiaoxue Wang, Jiadong Li, Haizhou |
| author_facet | Tao, Ruijie Qian, Xinyuan Das, Rohan Kumar Gao, Xiaoxue Wang, Jiadong Li, Haizhou |
| contents | Audio-visual active speaker detection (AV-ASD) aims to identify which visible face is speaking in a scene with one or more persons. Most existing AV-ASD methods prioritize capturing speech-lip correspondence. However, there is a noticeable gap in addressing the challenges from real-world AV-ASD scenarios. Due to the presence of low-quality noisy videos in such cases, AV-ASD systems without a selective listening ability are short of effectively filtering out disruptive voice components from mixed audio inputs. In this paper, we propose a Multi-modal Speaker Extraction-to-Detection framework named `MuSED', which is pre-trained with audio-visual target speaker extraction to learn the denoising ability, then it is fine-tuned with the AV-ASD task. Meanwhile, to better capture the multi-modal information and deal with real-world problems such as missing modality, MuSED is modelled on the time domain directly and integrates the multi-modal plus-and-minus augmentation strategy. Our experiments demonstrate that MuSED substantially outperforms the state-of-the-art AV-ASD methods and achieves 95.6% mAP on the AVA-ActiveSpeaker dataset, 98.3% AP on the ASW dataset, and 97.9% F1 on the Columbia AV-ASD dataset, respectively. We will publicly release the code in due course. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00861 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Real-World Active Speaker Detection with Multi-Modal Extraction Pre-Training Tao, Ruijie Qian, Xinyuan Das, Rohan Kumar Gao, Xiaoxue Wang, Jiadong Li, Haizhou Audio and Speech Processing Image and Video Processing Audio-visual active speaker detection (AV-ASD) aims to identify which visible face is speaking in a scene with one or more persons. Most existing AV-ASD methods prioritize capturing speech-lip correspondence. However, there is a noticeable gap in addressing the challenges from real-world AV-ASD scenarios. Due to the presence of low-quality noisy videos in such cases, AV-ASD systems without a selective listening ability are short of effectively filtering out disruptive voice components from mixed audio inputs. In this paper, we propose a Multi-modal Speaker Extraction-to-Detection framework named `MuSED', which is pre-trained with audio-visual target speaker extraction to learn the denoising ability, then it is fine-tuned with the AV-ASD task. Meanwhile, to better capture the multi-modal information and deal with real-world problems such as missing modality, MuSED is modelled on the time domain directly and integrates the multi-modal plus-and-minus augmentation strategy. Our experiments demonstrate that MuSED substantially outperforms the state-of-the-art AV-ASD methods and achieves 95.6% mAP on the AVA-ActiveSpeaker dataset, 98.3% AP on the ASW dataset, and 97.9% F1 on the Columbia AV-ASD dataset, respectively. We will publicly release the code in due course. |
| title | Enhancing Real-World Active Speaker Detection with Multi-Modal Extraction Pre-Training |
| topic | Audio and Speech Processing Image and Video Processing |
| url | https://arxiv.org/abs/2404.00861 |