The Moodo dataset: Integrating user context with emotional and color perception of music for affective music information retrieval
| Autor: | Matevž Pesek, Alenka Kavčič, Matija Marolt, Gregor Strle |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Visual Arts and Performing Arts
Multimedia Color vision media_common.quotation_subject 05 social sciences Context (language use) 02 engineering and technology computer.software_genre Sensor fusion 050105 experimental psychology Perception 0202 electrical engineering electronic engineering information engineering Music information retrieval 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Affective computing Psychology computer Music Music emotion recognition media_common Cognitive psychology Connotation |
| Zdroj: | Journal of New Music Research. 46:246-260 |
| ISSN: | 1744-5027 0929-8215 |
| Popis: | This paper presents a new multimodal dataset Moodo that can aid the development of affective music information retrieval systems. Moodo’s main novelties are a multimodal approach that links emotional and color perception to music and the inclusion of user context. Analysis of the dataset reveals notable differences in emotion-color associations and their valence-arousal ratings in non-music and music context. We also show differences in ratings of perceived and induced emotions, especially for those with perceived negative connotation, as well as the influence of genre and user context on perception of emotions. By applying an intermediate data fusion model, we demonstrate the importance of user profiles for predictive modeling in affective music information retrieval scenarios. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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