Deep Composer Classification Using Symbolic Representation

2020/10/02 by Sunghyeon Kim, Kim, Sunghyeon, Hyeyoon Lee +7 · 1 citation
Arts and Humanities · Computer Science · #Audio and Speech Processing (eess.AS) #Diverse Musicological Studies #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Music Technology and Sound Studies #Music and Audio Processing #Sound (cs.SD) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2010.00823

openalex publication_date 2020/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this study, we train deep neural networks to classify composer on a symbolic domain. The model takes a two-channel two-dimensional input, i.e., onset and note activations of time-pitch representation, which is converted from MIDI recordings and performs a single-label classification. On the experiments conducted on MAESTRO dataset, we report an F1 value of 0.8333 for the classification of 13~classical composers.

Citations

• Enabling Factorized Piano Music Modeling and Generation with the MAESTRO\n Dataset

Cited by

• Style-based Composer Identification and Attribution of Symbolic Music Scores: a Systematic Survey

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