國立虎尾科技大學 |

Neural decoding of subjective music listening experiences.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Neural decoding of subjective music listening experiences./
作者:	Thompson, Jessica.
面頁冊數:	1 online resource (83 pages)
附註:	Source: Masters Abstracts International, Volume: 53-05.
標題:	Music. -
電子資源:	click for full text (PQDT)
ISBN:	9781321152821

Neural decoding of subjective music listening experiences.
Thompson, Jessica.

Neural decoding of subjective music listening experiences. - 1 online resource (83 pages)

Source: Masters Abstracts International, Volume: 53-05.

Thesis (A.M.)--Dartmouth College, 2013.

Includes bibliographical references

How the human auditory system facilitates musical experience is not well understood. This thesis considers a computational investigation of the neural basis of musical experience alongside a discussion of physicality in experimental music, acoustic phenomenology, and computational methodologies. I argue that the study of the neural mechanisms underlying auditory experience is a key component in an interdisciplinary effort towards a phenomenological understanding of music and advocate for a phenomenological and computational approach to the design of psychological experiments. As a demonstration of this approach, I collected several hours of electroencephalography (EEG) data from only one subject: a highly trained musician and composer. The stimuli consisted of natural musical audio from a wide variety of styles, most of which was selected by the subject to be familiar and enjoyable. I investigated the extent to which various acoustic and musical features could be predicted from the continuous, single-trial EEG signal. To construct EEG features, independent component analysis was performed on the 64 channels from each session. The spatial maps of sufficiently stable and dipolar components were clustered and averaged. Finally, a short-time Fourier transform was applied to all accepted clusters and the spectrograms were concatenated into feature vectors. A linear multivariate regression model was trained to predict audio features from the EEG features. The model was evaluated in a leave-one-track-out cross validation and compared to a random baseline model which made predictions by sampling random frames from the training set. The root mean squared error of the regression model was significantly less than that of the random model for all audio features. Since there were no repetitions of the stimuli, these results cannot be explained by overfitting and must represent a general mapping between musical audio and the subject's electrical brain activity. Future work will experiment with various non-linear regression techniques and a behavioral experiment will be conducted to investigate the musical experiences evoked by the reconstructed audio.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781321152821Subjects--Topical Terms:

649088
Music.
Index Terms--Genre/Form:

554714
Electronic books.

Neural decoding of subjective music listening experiences.
LDR:03312ntm a2200337K 4500 001 913149
005 20180614071649.5
006 m o u
007 cr mn||||a|a||
008 190606s2013 xx obm 000 0 eng d
020 $a 9781321152821
035 $a (MiAaPQ)AAI1564146
035 $a (MiAaPQ)dartmouth:10513
035 $a AAI1564146
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Thompson, Jessica. $3 1185842
245 1 0 $a Neural decoding of subjective music listening experiences.
264 0 $c 2013
300 $a 1 online resource (83 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 53-05.
500 $a Adviser: Michael A. Casey.
502 $a Thesis (A.M.)--Dartmouth College, 2013.
504 $a Includes bibliographical references
520 $a How the human auditory system facilitates musical experience is not well understood. This thesis considers a computational investigation of the neural basis of musical experience alongside a discussion of physicality in experimental music, acoustic phenomenology, and computational methodologies. I argue that the study of the neural mechanisms underlying auditory experience is a key component in an interdisciplinary effort towards a phenomenological understanding of music and advocate for a phenomenological and computational approach to the design of psychological experiments. As a demonstration of this approach, I collected several hours of electroencephalography (EEG) data from only one subject: a highly trained musician and composer. The stimuli consisted of natural musical audio from a wide variety of styles, most of which was selected by the subject to be familiar and enjoyable. I investigated the extent to which various acoustic and musical features could be predicted from the continuous, single-trial EEG signal. To construct EEG features, independent component analysis was performed on the 64 channels from each session. The spatial maps of sufficiently stable and dipolar components were clustered and averaged. Finally, a short-time Fourier transform was applied to all accepted clusters and the spectrograms were concatenated into feature vectors. A linear multivariate regression model was trained to predict audio features from the EEG features. The model was evaluated in a leave-one-track-out cross validation and compared to a random baseline model which made predictions by sampling random frames from the training set. The root mean squared error of the regression model was significantly less than that of the random model for all audio features. Since there were no repetitions of the stimuli, these results cannot be explained by overfitting and must represent a general mapping between musical audio and the subject's electrical brain activity. Future work will experiment with various non-linear regression techniques and a behavioral experiment will be conducted to investigate the musical experiences evoked by the reconstructed audio.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Music. $3 649088
650 4 $a Neurosciences. $3 593561
650 4 $a Cognitive psychology. $3 556029
655 7 $a Electronic books. $2 local $3 554714
690 $a 0413
690 $a 0317
690 $a 0633
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Dartmouth College. $b Digital Musics. $3 1185843
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1564146 $z click for full text (PQDT)