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dc.contributor.authorWONG, WAI KIN (黃偉健)-
dc.identifier.citationWONG, W. K. (2015). User Customization for Music Emotion Classification using Online Sequential Extreme Learning Machine (Outstanding Academic Papers by Students (OAPS)). Retrieved from University of Macau, Outstanding Academic Papers by Students Repository.en_US
dc.description.abstractMusic is an art composed by sound. Music emotion recognition as a research topic stands on different areas such as psychology, musicology. The purpose of this work is to give a recommendation of music to the user by recognizing music emotion using machine learning algorithm. In order to take the music emotion recognition, a set of musical characteristics generated by MIR Tool Box has been used. Several machine learning algorithms are used and compared in this work. For traditional method such as k-nearest neighbour classifier (k-NN classifier) and state-of-the-art neural network such as support vector machine (SVM) and extreme learning machine (ELM). For the recognition result, it cannot get a full accuracy for every user. To improve the result, the online sequential extreme learning machine (OSELM) is used to learn one by one with a fixed size of new data for the user reported result then updating the model using the latest data.en_US
dc.titleUser Customization for Music Emotion Classification using Online Sequential Extreme Learning Machineen_US
dc.contributor.departmentDepartment of Computer and Information Scienceen_US
dc.description.instructorProf. VONG, CHI MANen_US
dc.contributor.facultyFaculty of Science and Technologyen_US
dc.description.programmeBachelor of Science in Computer Scienceen_US
Appears in Collections:FST OAPS 2015

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