skip to main content


Title: Baby Shark to Barracuda: Analyzing Children’s Music Listening Behavior
Music is an important part of childhood development, with online music listening platforms being a significant channel by which children consume music. Children’s offline music listening behavior has been heavily researched, yet relatively few studies explore how their behavior manifests online. In this paper, we use data from LastFM 1 Billion and the Spotify API to explore online music listening behavior of children, ages 6–17, using education levels as lenses for our analysis. Understanding the music listening behavior of children can be used to inform the future design of recommender systems.  more » « less
Award ID(s):
1751278
NSF-PAR ID:
10316668
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Fifteenth ACM Conference on Recommender Systems
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Research Highlights

    Children and adults conceptually and perceptually categorize speech and song from age 4.

    Listeners use F0 instability, harmonicity, spectral flux, and utterance duration to determine whether vocal stimuli sound like song.

    Acoustic cue weighting changes with age, becoming adult‐like at age 8 for perceptual categorization and at age 12 for conceptual differentiation.

    Young children are still learning to categorize speech and song, which leaves open the possibility that music‐ and language‐specific skills are not so domain‐specific.

     
    more » « less
  2. The dominant research strategy within the field of music perception and cognition has typically involved new data collection and primary analysis techniques. As a result, numerous information-rich yet underexplored datasets exist in publicly accessible online repositories. In this paper we contribute two secondary analysis methodologies to overcome two common challenges in working with previously collected data: lack of participant stimulus ratings and lack of physiological baseline recordings. Specifically, we focus on methodologies that unlock previously unexplored musical preference questions. Preferred music plays important roles in our personal, social, and emotional well-being, and is capable of inducing emotions that result in psychophysiological responses. Therefore, we select the Study Forrest dataset “auditory perception” extension as a case study, which provides physiological and self-report demographics data for participants (N = 20) listening to clips from different musical genres. In Method 1, we quantitatively model self-report genre preferences using the MUSIC five-factor model: a tool recognized for genre-free characterization of musical preferences. In Method 2, we calculate synthetic baselines for each participant, allowing us to compare physiological responses (pulse and respiration) across individuals. With these methods, we uncover average changes in breathing rate as high as 4.8%, which correlate with musical genres in this dataset (p < .001). High-level musical characteristics from the MUSIC model (mellowness and intensity) further reveal a linear breathing rate trend among genres (p < .001). Although no causation can be inferred given the nature of the analysis, the significant results obtained demonstrate the potential for previous datasets to be more productively harnessed for novel research.

     
    more » « less
  3. Music is a central part of adolescent life, and the connections between music, science, and math are vast and deep-rooted in history. In particular, the relationship between sound and the science of waves. This positions musical sound as an ideal avenue for students to explore and connect with science. Listening to Waves (LTW) is a program that introduces adolescents to the physics and technology of music and sound with the goal of improving their attitudes toward science. For this, LTW creates web applications designed to explore and create sound in a playful manner and integrates those applications with hands-on exploration of the physical sonic world. In the case study described in this article, LTW partnered with a large middle school serving low-income and underrepresented students, trained the teachers to use the web applications and associated curriculum (Minces 2021), and worked directly with eighth-grade student participants. Students enjoyed the program and participated enthusiastically. Pre-post surveys indicate that program participation improved the students’ attitudes toward science, including their intention to pursue a science career and their perception of themselves as capable of doing science. 
    more » « less
  4. The neuroscience of music and music-based interventions (MBIs) is a fascinating but challenging research field. While music is a ubiquitous component of every human society, MBIs may encompass listening to music, performing music, music-based movement, undergoing music education and training, or receiving treatment from music therapists. Unraveling the brain circuits activated and influenced by MBIs may help us gain better understanding of the therapeutic and educational values of MBIs by gathering strong research evidence. However, the complexity and variety of MBIs impose unique research challenges. This article reviews the recent endeavor led by the National Institutes of Health to support evidence-based research of MBIs and their impact on health and diseases. It also highlights fundamental challenges and strategies of MBI research with emphases on the utilization of animal models, human brain imaging and stimulation technologies, behavior and motion capturing tools, and computational approaches. It concludes with suggestions of basic requirements when studying MBIs and promising future directions to further strengthen evidence-based research on MBIs in connections with brain circuitry. SIGNIFICANCE STATEMENT Music and music-based interventions (MBI) engage a wide range of brain circuits and hold promising therapeutic potentials for a variety of health conditions. Comparative studies using animal models have helped in uncovering brain circuit activities involved in rhythm perception, while human imaging, brain stimulation, and motion capture technologies have enabled neural circuit analysis underlying the effects of MBIs on motor, affective/reward, and cognitive function. Combining computational analysis, such as prediction method, with mechanistic studies in animal models and humans may unravel the complexity of MBIs and their effects on health and disease. 
    more » « less
  5. Abstract

    Listening to music is an enjoyable behaviour that engages multiple networks of brain regions. As such, the act of music listening may offer a way to interrogate network activity, and to examine the reconfigurations of brain networks that have been observed in healthy aging. The present study is an exploratory examination of brain network dynamics during music listening in healthy older and younger adults. Network measures were extracted and analyzed together with behavioural data using a combination of hidden Markov modelling and partial least squares. We found age- and preference-related differences in fMRI data collected during music listening in healthy younger and older adults. Both age groups showed higher occupancy (the proportion of time a network was active) in a temporal-mesolimbic network while listening to self-selected music. Activity in this network was strongly positively correlated with liking and familiarity ratings in younger adults, but less so in older adults. Additionally, older adults showed a higher degree of correlation between liking and familiarity ratings consistent with past behavioural work on age-related dedifferentiation. We conclude that, while older adults do show network and behaviour patterns consistent with dedifferentiation, activity in the temporal-mesolimbic network is relatively robust to dedifferentiation. These findings may help explain how music listening remains meaningful and rewarding in old age.

     
    more » « less