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NLP-based music processing for composer classification.

Somrudee Deepaisarn1, Sirawit Chokphantavee2, Sorawit Chokphantavee2

  • 1Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, 12120, Thailand. s.deepaisarn@gmail.com.

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This study introduces a novel method for music composer classification using natural language processing techniques like SentencePiece and Word2vec. The musical word/subword vector standard deviation proved highly effective, achieving perfect composer classification scores.

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Area of Science:

  • Digital Music Processing
  • Computational Musicology
  • Machine Learning

Background:

  • Classifying music by composer is difficult due to flexible musical structures and subjective interpretation.
  • Existing methods may not fully capture the nuances of musical composition.
  • Virtuosic piano music data from MIDI and audio sources were used.

Purpose of the Study:

  • To develop an innovative approach for representing musical pieces using natural language processing (NLP) techniques.
  • To explore the use of SentencePiece and Word2vec for creating musical word/subword vectors.
  • To evaluate the effectiveness of this representation scheme for composer classification.

Main Methods:

  • Utilized pitch and duration as key musical features.
  • Applied SentencePiece and Word2vec to represent melodies as musical word/subword vectors.
  • Employed k-nearest neighbors, random forest, logistic regression, support vector machines, and multilayer perceptron for classification.
  • Varied feature extraction methods, classification algorithms, and music window sizes.

Main Results:

  • Classification performance was significantly influenced by feature extraction methods.
  • The musical word/subword vector standard deviation emerged as the most effective feature.
  • Achieved a high F1-score of 1.00 for composer classification.
  • No significant performance differences were found among the tested classification models.

Conclusions:

  • The proposed NLP-based approach effectively represents musical pieces for composer identification.
  • Musical word/subword vector standard deviation is a powerful feature for this task.
  • The method demonstrates high potential for digital music processing and music information retrieval.