Using Musical Structure to Enhance Automatic Chord Transcription

Chord extraction from audio is a well-established music computing task, and many valid approaches have been presented in recent years that use different chord templates, smoothing techniques and musical context models. The present work shows that additional exploitation of the repetitive structure of songs can enhance chord extraction, by combining chroma information from multiple occurrences of the same segment type. To justify this claim we modify an existing chord labelling method, providing it with manual or automatic segment labels, and compare chord extraction results on a collection of 125 songs to baseline methods without segmentation information. Our method results in consistent and more readily readable chord labels and provides a statistically significant boost in label accuracy.