Deep symbolic processing of human-performed musical sequences

Nahum Rangel, Salvador Godoy-Calderon, Hiram Calvo

Research output: Contribution to journalArticlepeer-review

Abstract

Artificial music tutors are needed for assisting a performer during his/her practice time whenever a human tutor is not available. But for these artificial tutors to be intelligent and fulfill the role of a music tutor, they have to be able to identify errors made by the performer while playing a musical sequence. This task is not a trivial one, since all musical activities are considered as open-ended domains. Therefore, not only there is no unique correct way of performing a musical sequence, but also the analysis made by the tutor has to consider the development level of the performer, the difficulty level of the performed musical sequence, and many other variables. This paper describes an ongoing research that uses cascading connected layers of symbolic processing as the core of a human-performed error identification and characterization module able to overcome the complexity of the studied open-ended domain.

Original languageEnglish
Pages (from-to)4739-4750
Number of pages12
JournalJournal of Intelligent and Fuzzy Systems
Volume42
Issue number5
DOIs
StatePublished - 2022

Keywords

  • Artificial intelligence
  • intelligent music tutors
  • musical sequence
  • symbolic processing

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