A Fully Convolutional Deep Auditory Model for Musical Chord Recognition

Description

Chord recognition systems depend on robust feature ex- traction pipelines. While these pipelines are traditionally hand-crafted, recent advances in end-to-end machine learn- ing 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 audi- tory 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 auto- matically and do not require expert knowledge for optimising parameters. We show that the learned auditory system ex- tracts musically interpretable features, and that the proposed chord recognition system achieves results on par or better than state-of-the-art algorithms.

Period	15 Sept 2016
Event title	IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Event type	Conference
Location	ItalyShow on map