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| Autores principales: | , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2510.10396 |
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| _version_ | 1866914097824530432 |
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| author | Guo, Wenxiang Pan, Changhao Zhu, Zhiyuan Hu, Xintong Zhang, Yu Tang, Li Yang, Rui Wang, Han Zhang, Zongbao Wang, Yuhan Chen, Yixuan Xu, Hankun Xu, Ke Fan, Pengfei Chen, Zhetao Yu, Yanhao Huang, Qiange Wu, Fei Zhao, Zhou |
| author_facet | Guo, Wenxiang Pan, Changhao Zhu, Zhiyuan Hu, Xintong Zhang, Yu Tang, Li Yang, Rui Wang, Han Zhang, Zongbao Wang, Yuhan Chen, Yixuan Xu, Hankun Xu, Ke Fan, Pengfei Chen, Zhetao Yu, Yanhao Huang, Qiange Wu, Fei Zhao, Zhou |
| contents | Humans rely on multisensory integration to perceive spatial environments, where auditory cues enable sound source localization in three-dimensional space. Despite the critical role of spatial audio in immersive technologies such as VR/AR, most existing multimodal datasets provide only monaural audio, which limits the development of spatial audio generation and understanding. To address these challenges, we introduce MRSAudio, a large-scale multimodal spatial audio dataset designed to advance research in spatial audio understanding and generation. MRSAudio spans four distinct components: MRSLife, MRSSpeech, MRSMusic, and MRSSing, covering diverse real-world scenarios. The dataset includes synchronized binaural and ambisonic audio, exocentric and egocentric video, motion trajectories, and fine-grained annotations such as transcripts, phoneme boundaries, lyrics, scores, and prompts. To demonstrate the utility and versatility of MRSAudio, we establish five foundational tasks: audio spatialization, and spatial text to speech, spatial singing voice synthesis, spatial music generation and sound event localization and detection. Results show that MRSAudio enables high-quality spatial modeling and supports a broad range of spatial audio research. Demos and dataset access are available at https://mrsaudio.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10396 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MRSAudio: A Large-Scale Multimodal Recorded Spatial Audio Dataset with Refined Annotations Guo, Wenxiang Pan, Changhao Zhu, Zhiyuan Hu, Xintong Zhang, Yu Tang, Li Yang, Rui Wang, Han Zhang, Zongbao Wang, Yuhan Chen, Yixuan Xu, Hankun Xu, Ke Fan, Pengfei Chen, Zhetao Yu, Yanhao Huang, Qiange Wu, Fei Zhao, Zhou Sound Humans rely on multisensory integration to perceive spatial environments, where auditory cues enable sound source localization in three-dimensional space. Despite the critical role of spatial audio in immersive technologies such as VR/AR, most existing multimodal datasets provide only monaural audio, which limits the development of spatial audio generation and understanding. To address these challenges, we introduce MRSAudio, a large-scale multimodal spatial audio dataset designed to advance research in spatial audio understanding and generation. MRSAudio spans four distinct components: MRSLife, MRSSpeech, MRSMusic, and MRSSing, covering diverse real-world scenarios. The dataset includes synchronized binaural and ambisonic audio, exocentric and egocentric video, motion trajectories, and fine-grained annotations such as transcripts, phoneme boundaries, lyrics, scores, and prompts. To demonstrate the utility and versatility of MRSAudio, we establish five foundational tasks: audio spatialization, and spatial text to speech, spatial singing voice synthesis, spatial music generation and sound event localization and detection. Results show that MRSAudio enables high-quality spatial modeling and supports a broad range of spatial audio research. Demos and dataset access are available at https://mrsaudio.github.io. |
| title | MRSAudio: A Large-Scale Multimodal Recorded Spatial Audio Dataset with Refined Annotations |
| topic | Sound |
| url | https://arxiv.org/abs/2510.10396 |