A software framework for musical data augmentation

Brian McFee, Eric J. Humphrey, Juan P. Bello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015
EditorsMeinard Muller, Frans Wiering
PublisherInternational Society for Music Information Retrieval
Pages248-254
Number of pages7
ISBN (Electronic)9788460688532
StatePublished - 2015
Event16th International Society for Music Information Retrieval Conference, ISMIR 2015 - Malaga, Spain
Duration: Oct 26 2015Oct 30 2015

Publication series

NameProceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015

Conference

Conference16th International Society for Music Information Retrieval Conference, ISMIR 2015
CountrySpain
CityMalaga
Period10/26/1510/30/15

ASJC Scopus subject areas

  • Music
  • Information Systems

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