A Fully Convolutional Deep Auditory Model for Musical Chord Recognition
This addresses chord recognition for music analysis, with incremental improvements over existing methods.
The paper tackled chord recognition in music by proposing a fully convolutional deep auditory model for feature extraction, achieving results on par or better than state-of-the-art algorithms.
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.