An end-to-end machine learning system for harmonic analysis of music

We present a new system for the harmonic analysis
of popular musical audio. It is focused on chord estimation,
although the proposed system additionally estimates the key
sequence and bass notes. It is distinct from competing approaches
in two main ways. Firstly, it makes use of a new improved
chromagram representation of audio that takes the human
perception of loudness into account. Furthermore, it is the first
system for joint estimation of chords, keys, and bass notes that is
fully based on machine learning, requiring no expert knowledge
to tune the parameters. This means that it will benefit from
future increases in available annotated audio files, broadening its
applicability to a wider range of genres. In all of three evaluation
scenarios, including a new one that allows evaluation on audio
for which no complete ground truth annotation is available, the
proposed system is shown to be faster, more memory efficient,
and more accurate than the state-of-the-art.