Mining Labeled Data from Web-Scale Collections for Vocal Activity Detection in Music


This work demonstrates an approach to generating strongly labeled data for vocal activity detection by pairing instrumental versions of songs with their original mixes. Though such pairs are rare, we find ample instances in a massive music collection for training deep convolutional networks at this task, achieving state of the art performance with a fraction of the human effort required previously. Our error analysis reveals two notable insights: imperfect systems may exhibit better temporal precision than human annotators, and should be used to accelerate annotation; and, machine learning from mined data can reveal subtle biases in the data source, leading to a better understanding of the problem itself. We also discuss future directions for the design and evolution of benchmarking datasets to rigorously evaluate AI systems.


August 2020 | ISMIR - International Society for Music Information Retrieval Conference

Data Cleansing with Contrastive Learning for Vocal Note Event Annotations

Gabriel Meseguer-Brocal, Rachel Bittner, Simon Durand, Brian Brost

July 2020 | IJCAI - International Joint Conference on Artificial Intelligence

Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling

Daniel Stoller, Mi Tian, Sebastian Ewert, and Simon Dixon