Towards a new reading experience via semantic fusion of text and music
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
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According to numbers of music cognitive experiments, moods or emotions in music could be categorical. Since mood classifications are commonly used to structure the large collections of music available on the Web, automatic discrimination between mood taxonomy of Chinese traditional music and Western classical music would be a valuable addition to music information retrieval (MIR) systems. In this paper, three content feature sets are extracted directly from the waveform audio clips, and then two mood taxonomy models are implemented. A Bayesian network is trained to classify the discrete mood categories. Finally, because the already-known algorithms have rarely applied to the Chinese traditional music, the comparative experimental result between Chinese and Western music evokes further research necessities.