Publicação
Bi-Modal Music Emotion Recognition: Novel Lyrical Features and Dataset
| Resumo: | This research addresses the role of audio and lyrics in the music emo- tion recognition. Each dimension (e.g., audio) was separately studied, as well as in a context of bimodal analysis. We perform classification by quadrant catego- ries (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. |
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| Autores principais: | Malheiro, Ricardo |
| Outros Autores: | Panda, Renato; Gomes, Paulo J. S.; Paiva, Rui Pedro |
| Assunto: | bimodal analysis music emotion recognition |
| Ano: | 2016 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Coimbra |
| Idioma: | inglês |
| Origem: | Estudo Geral - Universidade de Coimbra |
| Resumo: | This research addresses the role of audio and lyrics in the music emo- tion recognition. Each dimension (e.g., audio) was separately studied, as well as in a context of bimodal analysis. We perform classification by quadrant catego- ries (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. |
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