Bi-modal music emotion recognition: Novel lyrical features and dataset
| dc.contributor.author | Malheiro, Ricardo | |
| dc.contributor.author | Panda, Renato | |
| dc.contributor.author | Gomes, Paulo | |
| dc.contributor.author | Paiva, Rui | |
| dc.date.accessioned | 2017-10-13T14:33:03Z | |
| dc.date.available | 2017-10-13T14:33:03Z | |
| dc.date.issued | 2016 | |
| dc.description.abstract | This research addresses the role of audio and lyrics in the music emotion recognition. Each dimension (e.g., audio) was separately studied, as well as in a context of bimodal analysis. We perform classification by quadrant categories (4 classes). Our approach is based on several audio and lyrics state-of-the-art features, as well as novel lyric features. To evaluate our approach we create a ground-truth dataset. The main conclusions show that unlike most of the similar works, lyrics performed better than audio. This suggests the importance of the new proposed lyric features and that bimodal analysis is always better than each dimension. | pt_PT |
| dc.identifier.uri | http://repositorio.ismt.pt/handle/123456789/718 | |
| dc.language.iso | en | pt_PT |
| dc.publisher | 9th International Workshop on Music and Machine Learning – MML’2016 – in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases – ECML/PKDD 2016, October 2016 | pt_PT |
| dc.subject | Bimodal Analysis | pt_PT |
| dc.subject | Music Emotion Recognition | pt_PT |
| dc.title | Bi-modal music emotion recognition: Novel lyrical features and dataset | pt_PT |
| dc.type | Presentation | pt_PT |