Harmonising melodies: why do we add the bass line first
Harmonising melodies: why do we add the bass line first
We are taking an information theoretic approach to the question of the best way to harmonise melodies. Is it best to add the bass first, as has been traditionally the case? We describe software which uses statistical machine learning techniques to learn how to harmonise from a corpus of existing music. The software is able to perform the harmonisation task in various different ways. A performance comparison using the information theoretic measure cross-entropy is able to show that, indeed, the bass first approach appears to be best. We then use this overall strategy to investigate the performance of specialist models for the prediction of different musical attributes (such as pitch and note length) compared with single models which predict all attributes. We find that the use of specialist models affords a definite performance advantage. Final comparisons with a simpler model show that each has its pros and cons. Some harmonisations are presented which have been generated by some of the better performing models.
79-86
Whorley, Raymond
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Rhodes, Christophe
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Wiggins, Geraint
ebab7940-77dd-466e-b045-2ce8580b27c9
Pearce, Marcus
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12 June 2013
Whorley, Raymond
801f6e56-9fac-4e56-8fe5-03dd696e6f75
Rhodes, Christophe
916d6c0b-b09c-48cd-b4e4-64e6e0c5dc91
Wiggins, Geraint
ebab7940-77dd-466e-b045-2ce8580b27c9
Pearce, Marcus
3a1a6be0-6ea0-4292-912b-4ec75bf9d901
Whorley, Raymond, Rhodes, Christophe, Wiggins, Geraint and Pearce, Marcus
(2013)
Harmonising melodies: why do we add the bass line first.
Maher, Mary Lou Maher, Veale, Tony and Saunders, Rob
(eds.)
In Proceedings of the Fourth International Conference on Computational Creativity.
University of Sydney.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
We are taking an information theoretic approach to the question of the best way to harmonise melodies. Is it best to add the bass first, as has been traditionally the case? We describe software which uses statistical machine learning techniques to learn how to harmonise from a corpus of existing music. The software is able to perform the harmonisation task in various different ways. A performance comparison using the information theoretic measure cross-entropy is able to show that, indeed, the bass first approach appears to be best. We then use this overall strategy to investigate the performance of specialist models for the prediction of different musical attributes (such as pitch and note length) compared with single models which predict all attributes. We find that the use of specialist models affords a definite performance advantage. Final comparisons with a simpler model show that each has its pros and cons. Some harmonisations are presented which have been generated by some of the better performing models.
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Published date: 12 June 2013
Venue - Dates:
Fourth International Conference on<br/>Computational Creativity, University of Sydney, Sydney, Australia, 2013-06-12 - 2013-06-14
Identifiers
Local EPrints ID: 480193
URI: http://eprints.soton.ac.uk/id/eprint/480193
PURE UUID: b9a55cff-47be-4f5e-8233-057b23d1d6e2
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Date deposited: 01 Aug 2023 17:02
Last modified: 17 Mar 2024 04:19
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Contributors
Author:
Raymond Whorley
Author:
Christophe Rhodes
Author:
Geraint Wiggins
Author:
Marcus Pearce
Editor:
Mary Lou Maher Maher
Editor:
Tony Veale
Editor:
Rob Saunders
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