Use of Hidden Markov Models and Factored Language Models for Automatic Chord Recognition

This paper focuses on automatic extraction of acoustic chord sequences from a musical piece. Standard and factored language models are analyzed in terms of applicability to the chord recognition task. Pitch class profile vectors that represent harmonic information are extracted from the given audio signal. The resulting chord sequence is obtained by running a Viterbi decoder on trained hidden Markov models and subsequent lattice rescoring, applying the language model weight. We performed several experiments using the proposed technique. Results obtained on 175 manually-labeled songs provided an increase in accuracy of about 2%.