| dc.description.abstract | Automatic Chord Estimation (ACE) is a fundamental task in Music Information Retrieval (MIR), which has applications in both music performance and in research on other MIR tasks. The ACE task consists of segmenting a music recording or score and assigning a chord label to each segment. ACE has been a task in the MIREX competition for 10 years, but is not yet a solved problem: current methods seem to have reached a glass ceiling. Moreover, many recent methods are trained on a limited data set and consequently suffer from overfitting. In this thesis, I propose DECIBEL (DEtection of Chords Improved By Exploiting Linking symbolic formats), a novel system that utilizes multiple symbolic music representations in addition to audio in order to improve ACE on popular music.
The input for DECIBEL not only consists of the audio file, but also contains a set of MIDI and tab files that are obtained through web scraping and manually matched to the audio file. Given the audio file and matched MIDI and tab files, the system first estimates chord sequences from each file, using a representation-dependent method. For audio files, DECIBEL uses existing state-of-the-art audio ACE techniques: in my experiments, I use the output of the six ACE submissions from MIREX 2017, as well as a commercial state-of-the-art method. MIDI files are first aligned to the audio file, using a Dynamic Time Warping-based method; subsequently, chord sequences are estimated from the re-aligned MIDI files using an algorithm based on template matching. Tab files are first parsed, resulting in untimed chord sequences and then aligned to the audio, using an existing algorithm based on a Hidden Markov Model. In a final step, DECIBEL uses a data fusion method that integrates all estimated chord sequences into one final output sequence.
The main contributions of this thesis are twofold. First, by aligning different symbolic formats to audio, DECIBEL automatically creates a heterogeneous harmonic representation that enables large-scale cross-version analysis of popular music. Second, my results show that DECIBEL's data fusion method significantly improves each of the seven evaluated state-of-the-art audio ACE methods in terms of estimation accuracy. Exploiting the musical knowledge that is implicitly incorporated in MIDI and tab files, it breaks the observed glass ceiling, without requiring a lot of additional training, thereby prohibiting further overfitting to the existing chord annotations. | |
