Name
Abstract | ||
|---|---|---|
Music has a powerful influence on a listener's emotions. In this paper, we represent lyrics and chords in a shared vector space using a phrase-aligned chord-and-lyrics corpus. We show that models that use these shared representations predict a listener's emotion while hearing musical passages better than models that do not use these representations. Additionally, we conduct a visual analysis of these learnt shared vector representations and explain how they support existing theories in music. This work adds to our understanding of how lyrics and chords interact with one another in music and bears applications in music emotion recognition tasks and music information retrieval. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683735 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
distributed representations, text classification, music emotion recognition | Music information retrieval,Pattern recognition,Computer science,Musical,Music emotion recognition,Natural language processing,Artificial intelligence,Lyrics,Chord (music) | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Timothy Greer | 1 | 0 | 3.04 |
| karan singla | 2 | 4 | 4.52 |
| Benjamin Ma | 3 | 0 | 1.69 |
| Narayanan Shrikanth | 4 | 5558 | 439.23 |