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 a...
| Publicado en: | Canadian Journal of Experimental Psychology / Revue Canadienne de Psychologie Expérimentale Vol. 72; no. 3; pp. 153 - 171 |
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| Autores principales: | , |
| Formato: | Artículo |
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Canadian Psychological Association
Sp2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=131492986&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 131492986 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 11961961 CJX jtl: Canadian Journal of Experimental Psychology / Revue Canadienne de Psychologie Expérimentale issn: 11961961 maglogo: N pubinfo: dt: Sp2018 vid: 72 iid: 3 pid: 98 pub: Canadian Psychological Association artinfo: ui: 131492986 10.1037/cep0000135 ppf: 153 ppct: 18 formats: fmt: @attributes: type: P size: 2.4MB tig: atl: Key-Finding by Artificial Neural Networks That Learn About Key Profiles. aug: au: Dawson, Michael R. W. Zielinski, Jasen A. Z. su: Learning Music Task performance Algorithms Artificial neural networks Research evaluation Acoustic stimulation sug: subj: Learning Music Task performance Algorithms Artificial neural networks Research evaluation Acoustic stimulation keyword: artificial neural networks key-finding perceptrons réseaux neuraux artificiels sélection de clé artificial neural networks key-finding perceptrons réseaux neuraux artificiels sélection de clé ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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