IIIT Hyderabad develops model to predict traits through music-induced movement

Scientists at IIIT-H develop machine learning model in collaboration with Finnish varsity

By Author  |  Published: 17th Jul 2020  11:28 pm

Hyderabad: Scientists at the International Institute of Information Technology (IIIT) Hyderabad have developed a machine learning model that can look at the natural movement of listeners to music and predict their personalities besides cognitive styles.

Using an automated machine learning model, the scientists researched on ‘extraversion’ and ‘neuroticism’ dimensions in listeners. The idea was to study music-induced movement patterns that could predict individual traits, which could then be linked to music preference and recommendations.

“Natural swaying of the body and movement is a common response to music. And based on individuals’ movements to music, we can enhance their listening experience and make recommendations on what kind of music they might like in the future,” said Prof Petri Toiviainen, University of Jyväskylä, Finland and Dr Vinoo Alluri, who leads music research at IIIT-Hyderabad.

In a collaborative effort with the Department of Music, Arts and Culture, University of Jyväskylä, Jyväskylä, Finland, the study assessed participants’ personality and cognitive styles. The Big Five model was used where personality was ranked in terms of one’s openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism. For the cognitive styles, the participants were measured on the empathising quotient and systemising quotient.

From motion capture data, the researchers used computational methods to obtain position and velocity for each of the joints marked. It was found that features extracted from dynamic variations of the position of the joints in relation to each other were more accurate in predicting individual-specific traits than the velocity at which those joints were moving.

“The novel part of the study is the end-to-end architecture which uses natural movement to music to predict individual traits accurately and further explore particular joints which are relatively important in characterising those traits,” said Yudhik Agrawal, the first author of the study.

Apart from building a more personalised music recommendation system with these traits mapped to movement patterns, according to the scientists, the study serves as an initial step in the direction of autism research. “All participants in this study were healthy individuals. However, if we know that certain movements might be more typical to extreme systemisers, there are broader implications of this research in the domain of Autism Spectrum Disorder,” Dr Alluri said.

 


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