@Inproceedings{ju2019,
  Author = {Yaolong Ju, Samuel Howes, Cory McKay, Nathaniel Condit-Schultz, Jorge Calvo-Zaragoza and Ichiro Fujinaga},
  Booktitle = {Proceedings of the International Society for Music Information Retrieval},
  Month = {November},
  Pages = {862–869},
  Title = {An Interactive Workflow for Generating Chord Labels for Homorhythmic Music in Symbolic Formats},
  Year = {2019},
  Abstract = {Automatic harmonic analysis is challenging: rule-based models cannot account for every possible edge case, and manual annotation is expensive and sometimes inconsistent, undermining the training and evaluation of machine learning models. We present an interactive workflow to address these problems, and test it on Bach chorales. First, a rule-based model was used to generate preliminary, consistent chord labels in order to pre-train three machine learning models. These four models were grouped into an ensemble that generated chord labels by voting, achieving 91.4\% accuracy on a reserved test set. A domain expert then corrected only those chords that the ensemble did not agree on unanimously (20.9\% of the generated labels). Finally, we used these corrected annotations to re-train the machine learning models, and the resulting ensemble attained an accuracy of 93.5\% on the reserved test set, a 24.4\% reduction in the number of errors. This versatile interactive workflow can either work in a fully automatic way, or can capitalize on relatively minimal human involvement to generate higher-quality chord labels. It combines the consistency of rule-based models with the nuance of manual analysis to generate relatively inexpensive high-quality ground truth for training effective machine learning models.},
  Doi = {10.5281/zenodo.3527950},
  Localfile = {PDFs/ju2019.pdf}
}