000			02003cam a2200277zu 4500
001			88878173
003			FRCYB88878173
005			20250107161405.0
006			m o d
007			cr un
008			250107s2018 fr | o|||||0|0|||eng d
020			_a9781771992213
035			_aFRCYB88878173
040			_aFR-PaCSA _ben _c _erda
100	1		_aDawson, Michael R. W.
245	0	1	_aConnectionist Representations of Tonal Music _bDiscovering Musical Patterns by Interpreting Artifical Neural Networks _c['Dawson, Michael R. W.']
264		1	_bAthabasca University Press _c2018
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aDawson, Michael R. W.
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88878173 _qtext/html _a
520			_aPreviously, artificial neural networks have been used to capture only the informal properties of music. However, cognitive scientist Michael Dawson found that by training artificial neural networks to make basic judgments concerning tonal music, such as identifying the tonic of a scale or the quality of a musical chord, the networks revealed formal musical properties that differ dramatically from those typically presented in music theory. For example, where Western music theory identifies twelve distinct notes or pitch-classes, trained artificial neural networks treat notes as if they belong to only three or four pitch-classes, a wildly different interpretation of the components of tonal music. Intended to introduce readers to the use of artificial neural networks in the study of music, this volume contains numerous case studies and research findings that address problems related to identifying scales, keys, classifying musical chords, and learning jazz chord progressions. A detailed analysis of the internal structure of trained networks could yield important contributions to the field of music cognition.
999			_c42686 _d42686