Computational Modeling of Emotional Content in Music


We present a system designed to model characteristics which contribute to the emotional content of music. It creates n-gram models, Hidden Markov Models, and entropy-based models from corpora of musical selections representing various emotions. These models can be used both to identify emotional content and generate pieces representative of a target emotion. According to survey results, generated selections were able to communicate a desired emotion as effectively as human-generated compositions.

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