What Strikes the Strings of Your Heart?—Feature Mining for Music Emotion Analysis


Music can convey and evoke powerful emotions. This superb ability has not solely fascinated the final public however additionally attracted the researchers from different fields to get the relationship between music and emotion. Psychologists have indicated that some specific characters of rhythm, harmony, and melody can evoke certain sorts of emotions. Those hypotheses are based mostly on real life experience and proved by psychological paradigms on mortals. Aiming at the identical target, this paper intends to design a scientific and quantitative framework, and answer 3 widely interested queries: one) what are the intrinsic options embedded in music signal that basically evoke human emotions; a pair of) to what extent these features influence human emotions; and 3) whether or not the findings from computational models are per the present analysis results from psychology. We have a tendency to formulate these tasks as a multi-label dimensionality reduction downside and propose an algorithm referred to as multi-emotion similarity preserving embedding (ME-SPE). To adapt to the second-order music signals, we tend to extend ME-SPE to its bilinear version. The proposed techniques show sensible performance in 2 standard music emotion datasets. Moreover, they demonstrate some fascinating results for additional analysis in this interdisciplinary topic.

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