From this we conclude that Spotify provides some higher- level emotionally-relevant features. Combining Spotify and state-of-the-art sets leads to small improve- ments with fewer features (top5: +2.3%, top10: +1.1%), while not improving the highest results (100 features). ![]() However, the 12-feature set is unable to meet the performance of the features available in the state- of-the-art (58.5% vs. We verified that energy, va- lence and acousticness features from Spotify are highly relevant to MER. These are compared to emotionally-relevant features obtained previously, using feature ranking and emotion classification experiments. ![]() To this end, 12 Spotify API features were obtained for 704 of our 900-song dataset, annotated in terms of Russell’s quad- rants. H ere, we shed som e light on the suitability of the audio features provided by the Spotify API, the leading music streaming service, when applied to MER. Over the decades, myriads of audio features and recently feature-learning approaches have been tested in Music Emotion Recognition (MER) with scarce im- provements. Features are arguably the key factor to any machine learn- ing problem.
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