Name
Abstract | ||
|---|---|---|
We developed task-optimized deep neural networks (DNNs) that achieved state-of-the-art performance in different evaluation scenarios for automatic music tagging. These DNNs were subsequently used to probe the neural representations of music. Representational similarity analysis revealed the existence of a representational gradient across the superior temporal gyrus (STG). Anterior STG was shown to be more sensitive to low-level stimulus features encoded in shallow DNN layers whereas posterior STG was shown to be more sensitive to high-level stimulus features encoded in deep DNN layers. |
Year | Venue | DocType |
|---|---|---|
2016 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016) | Conference |
Volume | ISSN | Citations |
29 | 1049-5258 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Umut Güçlü | 1 | 88 | 10.86 |
| Thielen, Jordy | 2 | 0 | 0.34 |
| Michael Hanke | 3 | 205 | 37.31 |
| Marcel Van Gerven | 4 | 321 | 39.35 |
| Marcel Van Gerven | 5 | 321 | 39.35 |