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.
Abstract: In the era of extensive intersection between art and Artificial Intelligence (AI), such as image generation and fiction co-creation, AI for music remains relatively nascent, particularly in music understanding. This is evident in the limited work on deep music representations, the scarcity of large-scale datasets, and the absence of a universal and community-driven benchmark. To address this issue, we introduce the Music Audio Representation Benchmark for universaL Evaluation, termed MARBLE.