A software framework for musical data augmentation

Predictive models for music annotation tasks are practically limited by a paucity of well-annotated training data. In the broader context of large-scale machine learning, the concept of “data augmentation” — supplementing a training set with carefully perturbed samples — has emerged as an important component of robust systems. In this work, we develop a general software framework for augmenting annotated musical datasets, which will allow practitioners to easily expand training sets with musically motivated perturbations of both audio and annotations. As a proof of concept, we investigate the effects of data augmentation on the task of recognizing instruments in mixed signals.