A system for automatic chord transcription from audio using genre-specific hidden Markov models

We describe a system for automatic chord transcription from the raw audio using genre-specific hidden Markov models trained on audio-from-symbolic data. In order to avoid enormous amount of human labor required to manually annotate the chord labels for ground-truth, we use symbolic data such as MIDI files to automate the labeling process. In parallel, we synthesize the same symbolic files to provide the models with the sufficient amount of observation feature vectors along with the automatically generated annotations for training. In doing so, we build different models for various musical genres, whose model parameters reveal characteristics specific to their corresponding genre. The experimental results show that the HMMs trained on synthesized data perform very well on real acoustic recordings. It is also shown that when the correct genre is chosen, simpler, genre-specific model yields performance better than or comparable to that of more complex model that is genre-independent. Furthermore, we also demonstrate the potential application of the proposed model to the genre classification task.