MIRTracks: A Large-Scale Multi-Dimensional Multi-Track Music Dataset

This paper presents MIRTracks, a large-scale dataset containing 240 h of royalty-free multi-track audio, aiming to address the limitations of traditional music source separation datasets, including single-dimensional annotation and semantic information gaps. By integrating multi-dimensional musical information annotation with a semi-automated annotation pipeline, MIRTracks achieves high-quality semantic annotation across rock, electronic, and pop music genres. Experiments demonstrate that fine-tuning a small-scale model on this dataset significantly improves beat detection accuracy from 66.2% to 80.1%, reaching 91.0% of the performance of large-scale models.