Composer classification using melodic combinatorial n-grams

doi:10.1016/j.eswa.2024.123300

Composer classification using melodic combinatorial n-grams

2024-09-01

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Daniel Alejandro Pérez Alvarez

DOI：10.1016/j.eswa.2024.123300

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In the present study, we investigate the supervised problem of composer classification. From a set of compositions and a set of composers, we seek to assign each composition to the correct composer using machine learning and natural language processing techniques. Our objective focused on using the n -gram technique to create vector representations of musical compositions and classify them using the Support Vector Machines (SVM) classifier on a term -frequency matrix composed of the vectors of the compositions. Our representation takes into account melodic relationships between instruments in polyphonic pieces. We extract n -grams in melodic direction, allowing us to go from one instrument to another in the process, which aims to generate more robust n -grams and a greater quantity of occurrences of n -grams. We evaluate different classification models using feature filtering and varying hyperparameters such as the TF-IDF formula, among others. We test our method on a dataset made of string quartets by composers Haydn and Mozart, achieving results that improves upon previous state-of-the-art results.

Keywords:

Composer classification

Composer recognition

Composer identification

Composer attribution

Composer style

n-grams

Combinatorial n-grams

Mozart

Haydn

Composer classification using melodic combinatorial n-grams

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