Description: This dataset is a subset of the University of Rochester Music Performance (URMP) dataset. It contains image and audio files cut from the original URMP videos. We use a sliding window method to obtain the samples. The size of the sliding window is 0.5 seconds and the stride is 0.1 seconds. We use the first frame of each video chunk to represent the visual content of the sliding window. The audio files are in WAVE format with a sampling rate of 44 KHz and bit depth of 16 bits, stereo channel. The image files are 1080P (1080x1920). This dataset is used in our deep cross-modal audio-visual generation (paper).
If you use this dataset, please cite the following papers:
1) L. Chen, S. Srivastava, Z. Duan and C. Xu. Deep Cross-Modal Audio-Visual Generation. In Proc. of ACM International Conference on Multimedia Thematic Workshops, 2017.
2) B. Li, X. Liu, K. Dinesh, Z. Duan and G. Sharma. Creating A Musical Performance Dataset for Multimodal Music Analysis: Challenges, Insights, and Applications. arXiv:1612.08727, 2016. (The original URMP dataset paper.)