2015 | OriginalPaper | Buchkapitel
A Music Recommendation Method with Emotion Recognition Using Ranked Attributes
verfasst von : So-Hyun Park, Sun-Young Ihm, Wu-In Jang, Aziz Nasridinov, Young-Ho Park
Erschienen in: Computer Science and its Applications
Verlag: Springer Berlin Heidelberg
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Emotion recognition field can be useful for music discovery and recommendation, because emotions can precisely describe the actual habits of a listener. In this paper, we propose a new concept called Ranked Attributes that are useful to make reasonable music recommendations. More precisely, we propose to consider additional attributes to emotion, such as weather and time, and build a Ranked Attributes Tree (RAT) that enables to recommend a music piece based on a combination of all ranked attributes. In this paper, we describe the following parts of the proposed method: database design, voice and emotion recognition, and music recommendation.