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

Pitchclass2vec: Symbolic Music Structure Segmentation with Chord Embeddings