Audio chord labeling by musiological modeling and beat-synchronization

Automatic labeling of chords in original audio recordings is challenging due to heavy acoustic overlay by melody and percussion sections, detuning and arpeggios that demand for a measure-grid to assign notes to chords. Further chord labeling benefits from contextual information. In this respect we suggest applying an HMM framework incorporating a musiological model trained on 16 k songs and synchronization with the measure grid by IIR comb-filter banks for tempo detection, meter recognition, and on-beat tracking. Features base on pitch-tuned chromatic information. Extensive evaluation on 11 k chords of 7 h of MP3 compressed popular music demonstrates effectiveness over traditional correlation analysis and single measure classification by support vector machines.