Mel2Word: A Text-Based Melody Representation for Symbolic Music Analysis

Mel2Word: A Text-Based Melody Representation for Symbolic Music Analysis

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The purpose of this research is to present a natural language processing-based approach to symbolic music analysis.We propose Mel2Word, a text-based representation including pitch and rhythm information, and a new natural language processing-based melody segmentation algorithm.We first show how to create a melody dictionary using Byte Pair Encoding (BPE), which finds and merges the most frequent pairs that appear Seat Slide in a collection of melodies in a data-driven manner.The dictionary is then used to tokenize or segment a given melody.Utilizing various symbolic melody datasets, we conduct an exploratory Rocking Chair analysis and evaluate the classification performance of melody representation models on the MTC-ANN dataset.

A comparison with existing segmentation algorithms is also carried out.The result shows that the proposed model significantly improves classification performance in comparison to various melodic features and several existing segmentation algorithms.