Lara Aigmüller

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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(More)
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