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



music, audio analysis, cluster analysis


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.


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Author Biographies

Estela Ribeiro, Centro Universitario FEI, Sao Bernardo do Campo, Brazil

Estela Ribeiro received in 2015 the B. Sc. degree in mechanical engineering from FSA University Center, São Paulo, Brazil. Obtained the M.Sc. degree in electrical engineering from FEI University Center in 2017. Obtained the ph.D. degree in electrical engineering at FEI University Center, São Paulo, Brazil, in 2020. Since 2016, she has received a research fellowship from FEI, LNCT and CAPES to develop research activities on signal processing and pattern recognition. Her research interests include pattern recognition, cognitive perception and machine learning.

Carlos Eduardo Thomaz, FEI University Center

Carlos Thomaz received in 1993 the B.Sc. degree in electronic engineering from Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil. After working for six years in industry, he obtained the M.Sc. degree in electrical engineering from PUC-Rio in 1999. In October 2000, he joined the Department of Computing at Imperial College London where he obtained the Ph.D. degree in statistical pattern recognition in 2004. He joined the Department of Electrical Engineering, FEI University Center, São Paulo, Brazil, in 2005, as an Associate Professor, where he has been, since 2006, head of the Image Processing Laboratory. Since 2014 he has been Professor of Statistical Pattern Recognition at FEI. His research interests include pattern recognition, cognitive perception and machine learning. From 2015 to 2018, Professor Thomaz was awarded a Newton Advanced Fellowship from the Royal Society, UK.


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How to Cite

Ribeiro, E., & Thomaz, C. E. (2022). Less acoustic features means more statistical relevance: Disclosing the clustering behavior in music stimuli. IEEE Latin America Transactions, 20(4), 686–692. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/5908