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

TL;DR

This paper introduces a machine learning system for harmonic analysis of music audio, capable of estimating keys, chords, and bass notes simultaneously, using a novel perceptually-informed chromagram representation.

Contribution

It presents a fully machine learning-based system with a new chromagram representation, achieving state-of-the-art performance and broad applicability across music genres.

Findings

Fast and memory-efficient system

Achieves state-of-the-art accuracy

Applicable to various music genres

Abstract

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.