BEAT2AASIST model with layer fusion for ESDD 2026 Challenge
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866914205404233728 |
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| author | Chung, Sanghyeok Kim, Eujin Kim, Donggun Heo, Gaeun You, Jeongbin Lee, Nahyun Choi, Sunmook Han, Soyul Oh, Seungsang Kwak, Il-Youp |
| author_facet | Chung, Sanghyeok Kim, Eujin Kim, Donggun Heo, Gaeun You, Jeongbin Lee, Nahyun Choi, Sunmook Han, Soyul Oh, Seungsang Kwak, Il-Youp |
| contents | Recent advances in audio generation have increased the risk of realistic environmental sound manipulation, motivating the ESDD 2026 Challenge as the first large-scale benchmark for Environmental Sound Deepfake Detection (ESDD). We propose BEAT2AASIST which extends BEATs-AASIST by splitting BEATs-derived representations along frequency or channel dimension and processing them with dual AASIST branches. To enrich feature representations, we incorporate top-k transformer layer fusion using concatenation, CNN-gated, and SE-gated strategies. In addition, vocoder-based data augmentation is applied to improve robustness against unseen spoofing methods. Experimental results on the official test sets demonstrate that the proposed approach achieves competitive performance across the challenge tracks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15180 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BEAT2AASIST model with layer fusion for ESDD 2026 Challenge Chung, Sanghyeok Kim, Eujin Kim, Donggun Heo, Gaeun You, Jeongbin Lee, Nahyun Choi, Sunmook Han, Soyul Oh, Seungsang Kwak, Il-Youp Sound Machine Learning Recent advances in audio generation have increased the risk of realistic environmental sound manipulation, motivating the ESDD 2026 Challenge as the first large-scale benchmark for Environmental Sound Deepfake Detection (ESDD). We propose BEAT2AASIST which extends BEATs-AASIST by splitting BEATs-derived representations along frequency or channel dimension and processing them with dual AASIST branches. To enrich feature representations, we incorporate top-k transformer layer fusion using concatenation, CNN-gated, and SE-gated strategies. In addition, vocoder-based data augmentation is applied to improve robustness against unseen spoofing methods. Experimental results on the official test sets demonstrate that the proposed approach achieves competitive performance across the challenge tracks. |
| title | BEAT2AASIST model with layer fusion for ESDD 2026 Challenge |
| topic | Sound Machine Learning |
| url | https://arxiv.org/abs/2512.15180 |