MediaEval 2015: JKU-Tinnitus Approach to Emotion in Music Task
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چکیده
This paper describes the JKU-Tinnitus submission to the “Emotion in Music” task [1] of the 2015 MediaEval Benchmark. Given a set of manually annotated music and a set of features for each music file, machine learning algorithms are applied to estimate the development of emotional arousal and valence over the course of a piece of music. Our pipeline roughly contains feature extraction from the music files, a regression model and a Gauss filter as the final smoothing stage.
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تاریخ انتشار 2015