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Automatic Chord Estimation (ACE) is a fundamental task in Music InformationRetrieval (MIR) and has applications in both music performance and MIRresearch. The task consists of segmenting a music recording or score andassigning a chord label to each segment. Although it has been a task in theannual benchmarking evaluation MIREX for over 10 years, ACE is not yet a solvedproblem, since performance has stagnated and modern systems have started totune themselves to subjective training data. We propose DECIBEL, a new ACEsystem that exploits widely available MIDI and tab representations to improveACE from audio only. From an audio file and a set of MIDI and tab filescorresponding to the same popular music song, DECIBEL first estimates chordsequences. For audio, state-of-the-art audio ACE methods are used. MIDI filesare aligned to the audio, followed by a MIDI chord estimation step. Tab filesare transformed into untimed chord sequences and then aligned to the audio.Next, DECIBEL uses data fusion to integrate all estimated chord sequences intoone final output sequence. DECIBEL improves all tested state-of-the-art ACEmethods by over 3 percent on average. This result shows that the integration ofmusical knowledge from heterogeneous symbolic music representations is asuitable strategy for addressing challenging MIR tasks such as ACE.

DECIBEL: Improving Audio Chord Estimation for Popular Music by Alignment and Integration of Crowd-Sourced Symbolic Representations