Pitchclass2vec: Symbolic Music Structure Segmentation with Chord Embeddings

Structure perception is a fundamental aspect of music cognition in humans. Historically, the hierarchical
organization of music into structures served as a narrative device for conveying meaning, creating
expectancy, and evoking emotions in the listener. Thereby, musical structures play an essential role
in music composition, as they shape the musical discourse through which the composer organises his
ideas. In this paper, we present a novel music segmentation method, pitchclass2vec, based on symbolic
chord annotations, which are embedded into continuous vector representations using both natural
language processing techniques and custom-made encodings. Our algorithm is based on long-short term
memory (LSTM) neural network and outperforms the state-of-the-art techniques based on symbolic
chord annotations in the field.