Update README.md

README.md

@@ -28,10 +28,10 @@ Demos and are available at [MRSAudio](https://mrsaudio.github.io).
 
 The dataset of MRSAudio: A Large-Scale Multimodal Recorded Spatial Audio Dataset with Refined Annotations. Our dataset contains 500-hour large-scale multimodal spatial audio. It integrates high-fidelity spatial recordings with synchronized video, 3D pose tracking, and rich semantic annotations, enabling comprehensive modeling of real-world auditory scenes. The dataset comprises four subsets, each targeting distinct tasks and scenarios.
 
-- **MRSLife** (150 h): captures daily activities such as board games, cooking, and office work, using egocentric video and FOA audio annotated with sound events and speech transcripts.
-- **MRSSpeech** (200 h): includes binaural conversations from 50 speakers across diverse indoor environments, paired with video, 3D source positions, and complete scripts.
-- **MRSSing** (75 h): features high-quality solo singing performances in Chinese, English, German, and French by 20 vocalists, each aligned with time-stamped lyrics and corresponding musical scores.
-- **MRSMusic** (75 h) offers spatial recordings of 23 Traditional Chinese, Western and Electronic instruments, with symbolic score annotations that support learning-based methods for symbolic-to-audio generation and fine-grained localization.
+- **MRSLife** (129 h): captures daily activities such as board games, cooking, and office work, using egocentric video and FOA audio annotated with sound events and speech transcripts.
+- **MRSSpeech** (206 h): includes binaural conversations from 50 speakers across diverse indoor environments, paired with video, 3D source positions, and complete scripts.
+- **MRSSing** (80 h): features high-quality solo singing performances in Chinese, English, German, and French by 20 vocalists, each aligned with time-stamped lyrics and corresponding musical scores.
+- **MRSMusic** (69 h) offers spatial recordings of 23 Traditional Chinese, Western and Electronic instruments, with symbolic score annotations that support learning-based methods for symbolic-to-audio generation and fine-grained localization.
 
 Together, these four subsets support a broad spectrum of spatial audio research problems, including event detection, sound localization, and binaural or ambisonic audio generation. By pairing spatial audio with synchronized exocentric and egocentric video, geometric tracking, and detailed semantic labels, MRSAudio enables new research directions in multimodal spatial understanding and cross-modal generation. 
 
@@ -48,4 +48,8 @@ Together, these four subsets support a broad spectrum of spatial audio research
 ├── MRSSing
 ├── MRSSpeech
 └── README.md
-```
+```
+
+### News
+- Our Paper is accepted by NeurIPS 2025!
+- The refreshed version of our dataset is at [MRSAudio](https://huggingface.co/datasets/MRSAudio/MRSAudio)(still uploading).