Filip Korzeniowski, Gerhard Widmer,
"A Fully Convolutional Deep Auditory Model for Musical Chord Recognition"
: Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2016
Original Titel:
A Fully Convolutional Deep Auditory Model for Musical Chord Recognition
Sprache des Titels:
Deutsch
Original Buchtitel:
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Original Kurzfassung:
Chord recognition systems depend on robust feature extraction
pipelines. While these pipelines are traditionally
hand-crafted, recent advances in end-to-end machine learning
have begun to inspire researchers to explore data-driven
methods for such tasks. In this paper, we present a chord
recognition system that uses a fully convolutional deep auditory
model for feature extraction. The extracted features are
processed by a Conditional Random Field that decodes the
final chord sequence. Both processing stages are trained automatically
and do not require expert knowledge for optimising
parameters. We show that the learned auditory system extracts
musically interpretable features, and that the proposed
chord recognition system achieves results on par or better
than state-of-the-art algorithms.
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