Title:
The sound within: Learning audio features from electroencephalogram recordings of music listening
The sound within: Learning audio features from electroencephalogram recordings of music listening
| dc.contributor.advisor | Leslie, Grace | |
| dc.contributor.author | Vinay, Ashvala | |
| dc.contributor.committeeMember | Lerch, Alexander | |
| dc.contributor.committeeMember | Romberg, Justin | |
| dc.contributor.department | Music | |
| dc.date.accessioned | 2020-05-20T17:04:23Z | |
| dc.date.available | 2020-05-20T17:04:23Z | |
| dc.date.created | 2020-05 | |
| dc.date.issued | 2020-04-28 | |
| dc.date.submitted | May 2020 | |
| dc.date.updated | 2020-05-20T17:04:23Z | |
| dc.description.abstract | We look at the intersection of music, machine Learning and neuroscience. Specifically, we are interested in understanding how we can predict audio onset events by using the electroencephalogram response of subjects listening to the same music segment. We present models and approaches to this problem using approaches derived by deep learning. We worked with a highly imbalanced dataset and present methods to solve it - tolerance windows and aggregations. Our presented methods are a feed-forward network, a convolutional neural network (CNN), a recurrent neural network (RNN) and a RNN with a custom unrolling method. Our results find that at a tolerance window of 40 ms, a feed-forward network performed well. We also found that an aggregation of 200 ms suggested promising results, with aggregations being a simple way to reduce model complexity. | |
| dc.description.degree | M.S. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/62866 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | Music technology | |
| dc.subject | Machine learning | |
| dc.subject | Neuroimaging | |
| dc.subject | EEG | |
| dc.subject | Music information retireval | |
| dc.title | The sound within: Learning audio features from electroencephalogram recordings of music listening | |
| dc.type | Text | |
| dc.type.genre | Thesis | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | College of Design | |
| local.contributor.corporatename | School of Music | |
| local.relation.ispartofseries | Master of Science in Music Technology | |
| relation.isOrgUnitOfPublication | c997b6a0-7e87-4a6f-b6fc-932d776ba8d0 | |
| relation.isOrgUnitOfPublication | 92d2daaa-80f2-4d99-b464-ab7c1125fc55 | |
| relation.isSeriesOfPublication | bb52c603-2646-4dfa-a9b7-9f81b43c419a | |
| thesis.degree.level | Masters |