MOSA: Music Motion with Semantic Annotation Dataset for Cross-Modal Music Processing

TL;DR

The paper introduces MOSA, a large-scale, multi-modal music dataset with detailed annotations, enabling advanced research in cross-modal music retrieval and generation tasks.

Contribution

It presents the MOSA dataset, the largest cross-modal music dataset with note-level annotations, facilitating new research in music information retrieval and content generation.

Findings

Demonstrated tasks include beat, phrase, and expressive content detection from multiple data modalities.

Generated musicians' body motion from music audio using the dataset.

Provided a comprehensive benchmark for cross-modal music processing.

Abstract

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, phrase, 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).