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8 February 2017 Emotion detection model of Filipino music
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Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 102250J (2017)
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan
This research explored the creation of a model to detect emotion from Filipino songs. The emotion model used was based from Paul Ekman’s six basic emotions. The songs were classified into the following genres: kundiman, novelty, pop, and rock. The songs were annotated by a group of music experts based on the emotion the song induces to the listener. Musical features of the songs were extracted using jAudio while the lyric features were extracted by Bag-of- Words feature representation. The audio and lyric features of the Filipino songs were extracted for classification by the chosen three classifiers, Naïve Bayes, Support Vector Machines, and k-Nearest Neighbors. The goal of the research was to know which classifier would work best for Filipino music. Evaluation was done by 10-fold cross validation and accuracy, precision, recall, and F-measure results were compared. Models were also tested with unknown test data to further determine the models’ accuracy through the prediction results.
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Kathleen Alexis Noblejas, Daryl Arvin Isidro, and Mary Jane C. Samonte "Emotion detection model of Filipino music", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102250J (8 February 2017);

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