Abstract
Social interaction is one of the main channels to access reality and information about people. In this last years there is a growing interest in community websites that combine social interaction with music and entertainment exploration. Music is a language of emotions and music emotional recognition has been addressed by different disciplines (psychology, cognitive science and musicology). Aim of this work is to introduce a framework for music emotion recognition based on machine learning and soft computing techniques. First, musical emotional features are extract from audio songs and successively they are elaborated for classification or clustering. One user can submit a target song, representing his conceptual emotion, and to obtain a playlist of audio songs with similar emotional content. In the case of classification, a playlist is obtained from the songs of the same class. In the other case, the playlist is suggested by system exploiting the content of the audio songs and it could also contain songs of different classes. Several experiments are proposed to show the performance of the developed framework.
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Ciaramella, A., Vettigli, G. (2013). Machine Learning and Soft Computing Methodologies for Music Emotion Recognition. In: Apolloni, B., Bassis, S., Esposito, A., Morabito, F. (eds) Neural Nets and Surroundings. Smart Innovation, Systems and Technologies, vol 19. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35467-0_42
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DOI: https://doi.org/10.1007/978-3-642-35467-0_42
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