Data Cleansing with Contrastive Learning for Vocal Note Event Annotations
This addresses data quality issues for researchers and practitioners in Music Information Retrieval, but it is incremental as it adapts existing data cleansing techniques to a specific domain.
The paper tackles the problem of cleaning erroneous labels in music datasets with time-varying structured labels, proposing a novel data cleansing model using contrastive learning, and demonstrates that it improves transcription model accuracy significantly compared to using the original dataset.
Data cleansing is a well studied strategy for cleaning erroneous labels in datasets, which has not yet been widely adopted in Music Information Retrieval. Previously proposed data cleansing models do not consider structured (e.g. time varying) labels, such as those common to music data. We propose a novel data cleansing model for time-varying, structured labels which exploits the local structure of the labels, and demonstrate its usefulness for vocal note event annotations in music. %Our model is trained in a contrastive learning manner by automatically creating local deformations of likely correct labels. Our model is trained in a contrastive learning manner by automatically contrasting likely correct labels pairs against local deformations of them. We demonstrate that the accuracy of a transcription model improves greatly when trained using our proposed strategy compared with the accuracy when trained using the original dataset. Additionally we use our model to estimate the annotation error rates in the DALI dataset, and highlight other potential uses for this type of model.