Improving Audio Chord Estimation by Alignment and Integration of Crowd-Sourced Symbolic Music

Abstract

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 heterogeneous musical representations, specifically MIDI and tab files, to improve audio-based ACE methods. 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 0.5 to 13.6 percentage points. This result shows that the integration of crowd-sourced annotations from heterogeneous symbolic music representations using data fusion is a suitable strategy for addressing challenging MIR tasks such as ACE.

Keywords

• automatic chord estimation
• data fusion
• music alignment