In cross-modal music processing, translation between visual, auditory, and semantic content opens up new possibilities as well as challenges. The construction of such a transformative scheme depends upon a benchmark corpus with a comprehensive data infrastructure. In particular, the assembly of a large-scale cross-modal dataset presents major challenges. In this paper, we present the MOSA (Music mOtion with Semantic Annotation) dataset, which contains high quality 3-D motion capture data, aligned audio recordings, and note-by-note semantic annotations of pitch, beat, phrase, dynamic, articulation, and harmony for 742 professional music performances by 23 professional musicians, comprising more than 30 hours and 570 K notes of data. To our knowledge, this is the largest cross-modal music dataset with note-level annotations to date. To demonstrate the usage of the MOSA dataset, we present several innovative cross-modal music information retrieval (MIR) and musical content generation tasks, including the detection of beats, downbeats, phrases, and expressive contents from audio, video and motion data, and the generation of musicians' body motion from given music audio. The dataset and codes are available alongside this publication (https://github.com/yufenhuang/MOSA-Music-mOtion-and-Semantic-Annotation-dataset).

Organizing fingerings, i.e., choosing which fingers to press on which positions and strings, is a crucial step for playing the violin. As the violin fingering comprises several components, the mapping of a musical phrase to the corresponding fingering arrangement is not unique, and it requires comprehensive musical knowledge for organizing adequate fingerings. In this paper, we study the human-machine cooperative approach to the generation of violin fingering, aiming to build an intelligent system which can provide multiple generation paths and yield adaptable fingering arrangements. For this sake, we compile a new dataset with fingering annotations of multiple versions of performance, propose a deep neural network with conditions on the left-hand movement for fingering generation, and conduct an in-depth user study for detailed responses. Result shows that the proposed system can yield various fingering arrangements according to different performance requirements, though a single generation may not satisfy all the requirements at a time. This highlights the importance of multi-path and human-in-the-loop architecture for violin fingering generation.