Less acoustic features means more statistical relevance: Disclosing the clustering behavior in music stimuli

Identification of appropriate content-based features for the description of audio signals can provide a better repre-sentation of naturalistic music stimuli which, in recent years, have been used to understand how the human brain processes such information. In this work, an extensive clustering analysis has been carried out on a large and benchmark audio dataset to assess whether features commonly extracted in the literature are in fact statistically relevant. Our results show that not all of these well-known acoustic features might be statistically necessary. We also demonstrate quantitatively that, regardless of the musical genre, the same acoustic feature is selected to represent each cluster. This finding discloses that there is a general redundancy among the set of audio descriptors used, that does not depend on a particular music track or genre, allowing an expressive reduction of the number of features necessary to identify apropriate time instants on the audio for further brain signal processing of music stimuli.