A fully convolutional deep auditory model for musical chord recognition

Filip Korzeniowski, Gerhard Widmer
2016 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)  
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
more » ... ssing 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.
doi:10.1109/mlsp.2016.7738895 dblp:conf/mlsp/KorzeniowskiW16 fatcat:g22nozkezzfwji24qkx3nygmae