Pitchclass2vec: Symbolic Music Structure Segmentation with Chord Embeddings
This addresses the problem of automated music analysis for composers and musicologists, though it appears incremental as it builds on existing chord annotation methods.
The paper tackles music structure segmentation by introducing pitchclass2vec, a method that embeds symbolic chord annotations using NLP techniques and custom encodings with an LSTM neural network, outperforming state-of-the-art techniques in this field.
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.