An end-to-end machine learning system for harmonic analysis of music
  We present a new system for simultaneous estimation of keys, chords, and bass notes from music audio. It makes use of a novel chromagram representation of audio that takes perception of loudness into account. Furthermore, it is fully based on machine learning (instead of expert knowledge), such that it is potentially applicable to a wider range of genres as long as training data is available. As compared to other models, the proposed system is fast and memory efficient, while achieving state-of-the-art performance. 
Yizhao Ni, Matt Mcvicar, Raul Santos-Rodriguez and Tijl De Bie
cs.SD cs.AI cs.MM