Automatic Chord Transcription with Concurrent Recognition of Chord Symbols and Boundaries

This paper describes a method that recognizes musical chords from real-world audio signals in compact-disc recordings. The automatic recognition of musical chords is necessary for music information retrieval (MIR) systems, since the chord sequences of musical pieces capture the characteristics of their accompaniments. None of the previous methods can accurately recognize musical chords from complex audio signals that contain vocal and drum sounds. The main problem is that the chordboundary-detection and chord-symbol-identification processes are inseparable because of their mutual dependency. In order to solve this mutual dependency problem, our method generates hypotheses about tuples of chord symbols and chord boundaries, and outputs the most plausible one as the recognition result. The certainty of a hypothesis is evaluated based on three cues: acoustic features, chord progression patterns, and bass sounds. Experimental results show that our method successfully recognized chords in seven popular music songs; the average accuracy of the results was around 77%.