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