Improving audio chord transcription by exploiting harmonic and metric knowledge

Abstract

We present a new system for chord transcription from polyphonic musical audio that uses domain-specific knowledge about tonal harmony and metrical position to improve chord transcription performance. Low-level pulse and spectral features are extracted from an audio source using the Vamp plugin architecture. Subsequently, for each beat-synchronised chromagram we compute a list of chord candidates matching that chromagram, together with the confidence in each candidate. When one particular chord candidate matches the chromagram significantly better than all others, this chord is selected to represent the segment. However, when multiple chords match the chromagram similarly well, we use a formal music theoretical model of tonal harmony to select the chord candidate that best matches the sequence based on the surrounding chords. In an experiment we show that exploiting metrical and harmonic knowledge yields statistically significant chord transcription improvements on a corpus of 217 Beatles, Queen, and Zweieck songs.