Audio Chord estimation based on meter modeling and two-stage decoding

Abstract

In Music Information Retrieval (MIR) different approaches in modeling the meter structure of a song have been proposed and have been proved to be beneficial for the task of Audio Chord Estimation (ACE). In this paper we propose a novel approach that integrates the meter and beat information into the Hidden Markov Model (HMM) used for Audio Chord Estimation. In addition to the proposed meter model, we introduce also a modification in the inference procedure of the aforementioned Hidden Markov Model, in order to better capture the temporal correlation between chords progression. Experimental results show that the proposed approach is effective as the classical approaches in modeling the meter structure, but with a substantially reduced model complexity. Moreover, the proposed two-stage decoding procedure produces a significant improvement in the chords estimation accuracy.