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• W2238417843 abstract "This research proposes a framework for music mood classification that uses multiple and complementary information sources, namely, music audio, lyric text, and social tags associated with music pieces. This article presents the framework and a thorough evaluation of each of its components. Experimental results on a large data set of 18 mood categories show that combining lyrics and audio significantly outperformed systems using audio‐only features. Automatic feature selection techniques were further proved to have reduced feature space. In addition, the examination of learning curves shows that the hybrid systems using lyrics and audio needed fewer training samples and shorter audio clips to achieve the same or better classification accuracies than systems using lyrics or audio singularly. Last but not least, performance comparisons reveal the relative importance of audio and lyric features across mood categories." @default.
• W2238417843 created "2016-06-24" @default.
• W2238417843 creator A5018576124 @default.
• W2238417843 creator A5035323964 @default.
• W2238417843 creator A5052775912 @default.
• W2238417843 date "2016-01-13" @default.
• W2238417843 modified "2023-10-01" @default.
• W2238417843 title "A framework for evaluating multimodal music mood classification" @default.
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• W2238417843 doi "https://doi.org/10.1002/asi.23649" @default.
• W2238417843 hasPublicationYear "2016" @default.
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