A Large-Scale Study of Language Models for Chord Prediction
Filip Korzeniowski, David R. W. Sears, Gerhard Widmer
Abstract
We conduct a large-scale study of language models for chord prediction. Specifically, we compare N-gram models to various flavours of recurrent neural networks on a comprehensive dataset comprising all publicly available datasets of annotated chords known to us. This large amount of data allows us to systematically explore hyperparameter settings for the recurrent neural networks-a crucial step in achieving good results with this model class. Our results show not only a quantitative difference between the models, but also a qualitative one: in contrast to static N-gram models, certain RNN configurations adapt to the songs at test time. This finding constitutes a further step towards the development of chord recognition systems that are more aware of local musical context than what was previously possible.
BibTeX
@inproceedings{icassp2018_alargescalestudy,
title = {A Large-Scale Study of Language Models for Chord Prediction},
author = {Filip Korzeniowski and David R. W. Sears and Gerhard Widmer},
booktitle = {ICASSP 2018},
year = {2018}
}