MARBLE: Music Audio Representation Benchmark for Universal Evaluation
01/2023 – 06/2023
Supervised by Dr Emmanouil Benetos, Centre for Digital Music, Queen Mary University of London
- Designing the downstream tasks, datasets, evaluation metrics and state-of-the-art results.
- Implementing the mir_eval metrics with torchmetrics and developing utilisation for sequential tasks.
- Establishing a fair, reproducible and universal music information retrieval benchmark for future work.
- MARBLE website.
马英浩 (Nicolaus) MA Yinghao
PhD Student in AI & Music
MA Yinghao, PhD student in C4DM, QMUL. Research interests include music information retireval, self-supervised learning, music-related multimodal machine learning, and audio signal processing and matter.