Music can motivate many daily activities as it can regulate mood, increase productivity and sports performance, and raise spirits. However, we know little about how to recommend songs that are motivational for people given their contexts and activities. As a first step towards dealing with this issue, we adopt a theory-driven approach and operationalize the Brunel Music Rating Inventory (BMRI) to identify motivational qualities of music from the audio signal. When we look at frequently listened songs for 14 common daily activities through the lens of motivational music qualities, we find that they are clustered into three high-level latent activity groups: calm, vibrant, and intense. We show that our BMRI features can accurately classify songs in the three classes, thus enabling tools to select and recommend activity-specific songs from existing music libraries without any input required from user. We present the results of a preliminary user evaluation of our song recommender (called PepMusic) and discuss the implications for recommending songs for daily activities.

PepMusic: motivational qualities of songs for daily activities / Kim, Yongsung; Aiello, Luca Maria; Quercia, Daniele. - In: EPJ DATA SCIENCE. - ISSN 2193-1127. - 9:1(2020). [10.1140/epjds/s13688-020-0221-9]

PepMusic: motivational qualities of songs for daily activities

Quercia, Daniele
2020

Abstract

Music can motivate many daily activities as it can regulate mood, increase productivity and sports performance, and raise spirits. However, we know little about how to recommend songs that are motivational for people given their contexts and activities. As a first step towards dealing with this issue, we adopt a theory-driven approach and operationalize the Brunel Music Rating Inventory (BMRI) to identify motivational qualities of music from the audio signal. When we look at frequently listened songs for 14 common daily activities through the lens of motivational music qualities, we find that they are clustered into three high-level latent activity groups: calm, vibrant, and intense. We show that our BMRI features can accurately classify songs in the three classes, thus enabling tools to select and recommend activity-specific songs from existing music libraries without any input required from user. We present the results of a preliminary user evaluation of our song recommender (called PepMusic) and discuss the implications for recommending songs for daily activities.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2996129