Automatic difficulty level rating for guitar tablatures

Date of Publication

2016

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Computer Science

Subject Categories

Computer Sciences

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Rafael Cabredo

Defense Panel Member

Nelson Marcos
Conrado D. Ruiz

Abstract/Summary

This research tackles the problem of automating the ranking of a guitar tablature's di culty level. Building upon recent research, this project proposes several di culty features and investigates their in uence on a set of pre-rated set of pieces being used by an existing academic standard of music levels. The di culty features will be ranked according to their in uence on a music school's levelling criteria. Models for automatically rating tablature were built around these experiments with the goal of a web application that provides the di culty level of a tablature, with hopes of improving the ambiguous format that guitar tablatures present. The linear regression model chosen had an r-squared metric of 27.61% and was implemented in a tablature repository website.

Abstract Format

html

Language

English

Format

Electronic

Accession Number

CDTU022129

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

1 computer optical disc ; 4 3/4 in.

Keywords

Guitar music; Tablature (Music)

Embargo Period

1-26-2022

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