Estimating Note Phrase Aesthetic Similarity Using Feature-Based Taxicab Geometry
Musicology is a growing focus in computer science. The comparison of musical phrases and the assessment of their similarity is a basic skill for musicians but is a rarely explored subject in the context of computer science. Computer science has a large variety of measures for the task of assessing s...
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2018
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ph-ateneo-arc.discs-faculty-pubs-13062022-04-28T06:04:11Z Estimating Note Phrase Aesthetic Similarity Using Feature-Based Taxicab Geometry Samson, Aran V Coronel, Andrei D Musicology is a growing focus in computer science. The comparison of musical phrases and the assessment of their similarity is a basic skill for musicians but is a rarely explored subject in the context of computer science. Computer science has a large variety of measures for the task of assessing similarity of different data types but very few can be applied when assessing music. In this article, we present a novel approach of assessment, specifically designed for measuring the similarity of the aesthetic of note phrases based on features extracted using jSymbolic applied in the context of taxicab geometry. In this study, dissimilarity or distance is proposed to be a function of several factors including note occurrences, chromaticism, note motion, among other factors. This study's results may be used in the development of aesthetic-based algorithmic musical composition. 2018-02-01T08:00:00Z text https://archium.ateneo.edu/discs-faculty-pubs/293 https://ieeexplore.ieee.org/document/8376506 Department of Information Systems & Computer Science Faculty Publications Archīum Ateneo Feature extraction Music Measurement Digital art Media Tools Instruments computer music musical analysis distance metrics Computer Sciences Databases and Information Systems Musicology Theory and Algorithms |
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Feature extraction Music Measurement Digital art Media Tools Instruments computer music musical analysis distance metrics Computer Sciences Databases and Information Systems Musicology Theory and Algorithms |
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Feature extraction Music Measurement Digital art Media Tools Instruments computer music musical analysis distance metrics Computer Sciences Databases and Information Systems Musicology Theory and Algorithms Samson, Aran V Coronel, Andrei D Estimating Note Phrase Aesthetic Similarity Using Feature-Based Taxicab Geometry |
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Musicology is a growing focus in computer science. The comparison of musical phrases and the assessment of their similarity is a basic skill for musicians but is a rarely explored subject in the context of computer science. Computer science has a large variety of measures for the task of assessing similarity of different data types but very few can be applied when assessing music. In this article, we present a novel approach of assessment, specifically designed for measuring the similarity of the aesthetic of note phrases based on features extracted using jSymbolic applied in the context of taxicab geometry. In this study, dissimilarity or distance is proposed to be a function of several factors including note occurrences, chromaticism, note motion, among other factors. This study's results may be used in the development of aesthetic-based algorithmic musical composition. |
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text |
author |
Samson, Aran V Coronel, Andrei D |
author_facet |
Samson, Aran V Coronel, Andrei D |
author_sort |
Samson, Aran V |
title |
Estimating Note Phrase Aesthetic Similarity Using Feature-Based Taxicab Geometry |
title_short |
Estimating Note Phrase Aesthetic Similarity Using Feature-Based Taxicab Geometry |
title_full |
Estimating Note Phrase Aesthetic Similarity Using Feature-Based Taxicab Geometry |
title_fullStr |
Estimating Note Phrase Aesthetic Similarity Using Feature-Based Taxicab Geometry |
title_full_unstemmed |
Estimating Note Phrase Aesthetic Similarity Using Feature-Based Taxicab Geometry |
title_sort |
estimating note phrase aesthetic similarity using feature-based taxicab geometry |
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Archīum Ateneo |
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2018 |
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https://archium.ateneo.edu/discs-faculty-pubs/293 https://ieeexplore.ieee.org/document/8376506 |
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