A Discrete Mixture Model for Chord Labelling

Chord labels for recorded audio are in high demand both as an end product used by musicologists and hobby musicians and as an input feature for music similarity applications. Many past algorithms for chord labelling are based on chromagrams, but distribution of energy in chroma frames is not well understood. Furthermore, non-chord notes complicate chord estimation. We present a new approach which uses as a basis a relatively simple chroma model to represent short-time sonorities derived from melody range and bass range chromagrams. A chord is then modelled as a mixture of these sonorities, or subchords. We prove the practicability of the model by implementing a hidden Markov model (HMM) for chord labelling, in which we use the discrete subchord features as observations. We model gammadistributed chord durations by duplicate states in the HMM, a technique that had not been applied to chord labelling. We test the algorithm by five-fold cross-validation on a set of 175 hand-labelled songs performed by the Beatles. Accuracy figures compare very well with other state of the art approaches. We include accuracy specified by chord type as well as a measure of temporal coherence.