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

Abstract

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 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 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.