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Key-Finding by Artificial Neural Networks That Learn About Key Profiles

Key-Finding by Artificial Neural Networks That Learn About Key Profiles We explore the ability of a very simple artificial neural network, a perceptron, to assert the musical key of novel stimuli. First, perceptrons are trained to associate standardized key profiles (taken from 1 of 3 different sources) to different musical keys. After training, we measured perceptron accuracy in asserting musical keys for 296 novel stimuli. Depending upon which key profiles were used during training, perceptrons can perform as well as established key-finding algorithms on this task. Further analyses indicate that perceptrons generate higher activity in a unit representing a selected key and much lower activities in the units representing the competing keys that are not selected than does a traditional algorithm. Finally, we examined the internal structure of trained perceptrons and discovered that they, unlike traditional algorithms, assign very different weights to different components of a key profile. Perceptrons learn that some profile components are more important for specifying musical key than are others. These differential weights could be incorporated into traditional algorithms that do not themselves employ artificial neural networks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale American Psychological Association

Key-Finding by Artificial Neural Networks That Learn About Key Profiles

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References (115)

Publisher
American Psychological Association
Copyright
© 2017 Canadian Psychological Association
ISSN
1196-1961
eISSN
1878-7290
DOI
10.1037/cep0000135
pmid
28481568
Publisher site
See Article on Publisher Site

Abstract

We explore the ability of a very simple artificial neural network, a perceptron, to assert the musical key of novel stimuli. First, perceptrons are trained to associate standardized key profiles (taken from 1 of 3 different sources) to different musical keys. After training, we measured perceptron accuracy in asserting musical keys for 296 novel stimuli. Depending upon which key profiles were used during training, perceptrons can perform as well as established key-finding algorithms on this task. Further analyses indicate that perceptrons generate higher activity in a unit representing a selected key and much lower activities in the units representing the competing keys that are not selected than does a traditional algorithm. Finally, we examined the internal structure of trained perceptrons and discovered that they, unlike traditional algorithms, assign very different weights to different components of a key profile. Perceptrons learn that some profile components are more important for specifying musical key than are others. These differential weights could be incorporated into traditional algorithms that do not themselves employ artificial neural networks.

Journal

Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale – American Psychological Association

Published: Sep 8, 2018

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