Background: The acceptable noise level (ANL) is the maximum level of background noise that an individual is willing to accept while listening to speech. The type of background noise does not affect ANL results except for music.
Purpose: The purpose of this study was to determine if ANL differed due to music genre or music genre preference.
Research Design: A repeated-measures experimental design was employed.
Study Sample: Thirty-three young adults with normal hearing served as listeners.
Data Collection And Analysis: Most comfortable listening level and background noise level were measured to twelve-talker babble and five music samples from different genres: blues, classical, country, jazz, and rock. Additionally, music preference was evaluated via rank ordering of genre and by completion of the Short Test of Music Preference (STOMP) questionnaire.
Results: The ANL for music differed based on music genre; however, the difference was unrelated to music genre preference. Also, those with low ANLs tended to prefer the intense and rebellious music-preference dimension compared with those with high ANLs.
Conclusions: For instrumental music, ANL was lower for blues and rock music compared with classical, country, and jazz. The differences identified were not related to music genre preference; however, this finding may be related to the music-preference dimension of intense and rebellious music. Future work should evaluate the psychological variables that make up music-preference dimension to determine if these relate to our ANL.
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http://dx.doi.org/10.1055/a-1656-5996 | DOI Listing |
Psychol Res
January 2025
Department of Neurology and Clinical Neurophysiology Unit, Faculty of Medicine-Cairo University, Cairo, Egypt.
Introduction: Music is known to impact attentional state without conscious awareness. Listening to music encourages the brain to secrete neurotransmitters improving cognition and emotion.
Aim Of Work: Analysis of QEEG band width while listening to two music types, identifying different cortical areas activated and which genre has a similar effect to relaxed EEG.
JMIR Form Res
December 2024
School of Media and Journalism, Kent State University, Kent, OH, United States.
Background: The pervasiveness of drug culture has become evident in popular music and social media. Previous research has examined drug abuse content in both social media and popular music; however, to our knowledge, the intersection of drug abuse content in these 2 domains has not been explored. To address the ongoing drug epidemic, we analyzed drug-related content on Twitter (subsequently rebranded X), with a specific focus on lyrics.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Sport Studies, Faculty of Education Studies, Universiti Putra Malaysia, Selangor, Malaysia.
Music genres classification poses a formidable challenge as it necessitates capturing the intricate and varied characteristics of musical signals. In this study, an innovative approach is presented to classify the music genres using the Capsule Neural Network (CapsNet). The CapsNet model optimized by an advanced version of Triangulation Topology Aggregation Optimizer (ATTAO).
View Article and Find Full Text PDFSci Rep
December 2024
Arak Branch, Islamic Azad University, Arak, Iran.
Music genres classification has long been a challenging task in the field of Music Information Retrieval (MIR) due to the intricate and diverse nature of musical content. Traditional methods have struggled to accurately capture the complex patterns that differentiate one genre from another. However, recent advancements in deep learning have presented new opportunities to tackle this challenge.
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