@inproceedings{bc6fd1c6948a4f3781a60dc10308df40,
title = "A software framework for musical data augmentation",
abstract = "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.",
author = "Brian McFee and Humphrey, {Eric J.} and Bello, {Juan P.}",
note = "Funding Information: BM acknowledges support from the Moore-Sloan Data Science Environment at NYU. This material is partially based upon work supported by the National Science Foundation, under grant IIS-0844654. Funding Information: BM acknowledgessupport from the Moore-Sloan Data Science Environmentat NYU. This material is partially based upon work supported by the National Science Foundation, under grant IIS-0844654.; 16th International Society for Music Information Retrieval Conference, ISMIR 2015 ; Conference date: 26-10-2015 Through 30-10-2015",
year = "2015",
language = "English (US)",
series = "Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015",
publisher = "International Society for Music Information Retrieval",
pages = "248--254",
editor = "Meinard Muller and Frans Wiering",
booktitle = "Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015",
}