Abstract
Music conveys and evokes feeling. Many studies that correlate music with emotion have been done as people nowadays often prefer to listen to a certain song that suits their moods or emotion .This project present works on classifying emotion in music by exploiting vocal and instrumental part of a song. The final system is able to use musical features extracted from vocal part and instrumental part of a song, such as spectral centroid, spectral rolloff and zero-cross as to classify whether selected Malay popular music contain “sad” or “happy” emotion. Fuzzy k-NN (FKNN) and artificial neural network (ANN) are used in this system as a machine classifier. The percentages of emotion classified in Malay popular songs are expected to be higher when both features are applied.
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Misron, M.M., Rosli, N., Manaf, N.A., Halim, H.A. (2014). Music Emotion Classification (MEC): Exploiting Vocal and Instrumental Sound Features. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_51
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DOI: https://doi.org/10.1007/978-3-319-07692-8_51
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