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Music Perception and Appraisal: Cochlear Implant Users and Simulated Cochlear Implant Listening

Rose Wright, Rosalie M. Uchanski
2012 Journal of american academy of audiology  
Only one study has attempted to predict CI users' enjoyment and perception of music from the users' demographic variables and other perceptual skills (Gfeller et al., 2008) .  ...  For roughly half of the music perception tests, there are no statistically significant differences between the performance of the CI users and of the CIsim listeners.  ...  category when the CI users' listening-habits responses are from their pre-HL period.  ... 
doi:10.3766/jaaa.23.5.6 pmid:22533978 pmcid:PMC3400338 fatcat:qobw4yugdrchdivi5k4rzr2pgq

Music listening in everyday life: Devices, selection methods, and digital technology

Amanda E Krause, Adrian C North
2014 Psychology of Music  
Study 2 supported the first study in terms of identity, and demonstrated that a different pattern of variables predicted playlist listening from listening to music via shuffle.  ...  More generally, the findings suggest the utility of applying constructs from consumer psychology to everyday music listening behaviors.  ...  listening habits and technology use.  ... 
doi:10.1177/0305735614559065 fatcat:fyvgvfduwjagfjiyw2eh5in634


Manuel Cuadrado-García, María José Miquel-Romero, Juan D. Montoro-Pons
2019 Scientific Annals of Economics and Business  
First, objective variables such as demographics, music consumption habits, music genres and technology. Second, subjective variables such as motives and attitudes towards piracy.  ...  To this end we analyse the role of the variables defining the different segments of music users. In doing so, we have considered two main traits influencing the use of music.  ...  Demographic variables such as gender and age plus the use of mobile devices to listen to music are heavily associated to music downloading.  ... 
doi:10.47743/saeb-2019-0019 fatcat:uq373h3is5gflakxrcapnbu5aa

Predicting user demographics from music listening information

Thomas Krismayer, Markus Schedl, Peter Knees, Rick Rabiser
2018 Multimedia tools and applications  
We investigate to which extent the music listening habits of users of the social music platform can be used to predict their age, gender, and nationality.  ...  We conclude that personal information can be derived from music listening information, which indeed can help better tailoring recommendations, as we illustrate with the use case of a music recommender  ...  In contrast to these two existing works [21, 38] , our main contributions are: (i) we present a novel approach for the prediction of user traits from music listening habits that combines multiple sources  ... 
doi:10.1007/s11042-018-5980-y fatcat:wxebu7sc5ngfdh3ifuponoziim

Walk the Talk [chapter]

Denis Parra, Xavier Amatriain
2011 Lecture Notes in Computer Science  
Most of the approaches for understanding user preferences or taste are based on having explicit feedback from users.  ...  However, in many real-life situations we need to rely on implicit feedback such as the amount of times a user has bought an item or listened to a song.  ...  Using the results of our analysis, we create a predictive model in which we can predict a user rating, given information of how the user interacted with an item.  ... 
doi:10.1007/978-3-642-22362-4_22 fatcat:cnu3337tvvaqdoqva2tcmdjwpi

Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste

Zahra Nazari, Christophe Charbuillet, Johan Pages, Martin Laurent, Denis Charrier, Briana Vecchione, Ben Carterette
2020 Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval  
Recommender systems are increasingly used to predict and serve content that aligns with user taste, yet the task of matching new users with relevant content remains a challenge.  ...  Using music consumption behavior, we examine two main techniques in inferring Spotify users preferences over more than 200k podcasts.  ...  user's listening habits.  ... 
doi:10.1145/3397271.3401101 dblp:conf/sigir/NazariCPLCVC20 fatcat:uwiovcywbzdzfjjy5abo5dlzz4

Temporal Stability of Music Perception and Appraisal Scores of Adult Cochlear Implant Recipients

Kate Gfeller, Dingfeng Jiang, Jacob J. Oleson, Virginia Driscoll, John F. Knutson
2010 Journal of american academy of audiology  
After controlling for the baseline value, hearing aid use, months of use, music listening habits after implantation, and formal musical training in elementary school were significant predictors of FMR  ...  In contrast, data taken from single testing protocols of music perception and appraisal indicate that CIs are less than ideal in transmitting important structural features of music, such as pitch, melody  ...  Predictors related to musical training and experience included: music listening habits prior to implantation (MBQpre), music listening habits after implantation (MBQpost) (Gfeller et al, 2000b) , amount  ... 
doi:10.3766/jaaa.21.1.4 pmid:20085197 pmcid:PMC2844251 fatcat:tkdc6cxakvglzlawwuklw3ofma

Self-reported music perception is related to quality of life and self-reported hearing abilities in cochlear implant users

Christina Fuller, Rolien Free, Bert Maat, Deniz Başkent
2021 Cochlear Implants International  
A decline in music listening habits and a low rating of the quality of music after implantation are reported in DMBQ.  ...  Participants filled three questionnaires: (1) the Dutch Musical Background Questionnaire (DMBQ), which measures the music listening habits, the quality of the sound of music and the self-assessed perception  ...  Deniz Bas ¸kent is professor of speech perception in hearing impaired, and users of hearing aids and cochlear implant users at the University Medical Center Groningen, the Netherlands.  ... 
doi:10.1080/14670100.2021.1948716 pmid:34470590 fatcat:tznwn5z7ojaqpc3s4ssmjlvwty

Investigating country-specific music preferences and music recommendation algorithms with the LFM-1b dataset

Markus Schedl
2017 International Journal of Multimedia Information Retrieval  
It contains more than one billion music listening events created by more than 120,000 users of  ...  Basic demographic information and a selection of more elaborate listener-specific descriptors are included as well, for anonymized users.  ...  This strongly supports research in music recommendation, as evidenced by many publications that exploit data.  ... 
doi:10.1007/s13735-017-0118-y pmid:28357190 pmcid:PMC5350199 fatcat:diuxmfcvhnh2had5rknm7ryete

Modeling Popularity and Temporal Drift of Music Genre Preferences

Elisabeth Lex, Dominik Kowald, Markus Schedl
2020 Transactions of the International Society for Music Information Retrieval  
We adopt BLL u to model the listening habits and to predict the music genre preferences of three user groups: listeners of (i) niche, low-mainstream music, (ii) mainstream music, and (iii) medium-mainstream  ...  In this paper, we address the problem of modeling and predicting the music genre preferences of users.  ...  In this paper, we introduce a novel user modeling and genre prediction approach for users with different music consumption patterns and listening habits.  ... 
doi:10.5334/tismir.39 fatcat:2xcfyfgeencwrioxjvldbql7ci

An Improved Rating Mapping Algorithm for Music Recommender System

Xin Zhao, Xiao-meng Mao, Lian-hui Liu, Jun Zheng, Yan Liu
2017 DEStech Transactions on Engineering and Technology Research  
When the artist play count lies across two intervals, the higher rating is not appropriate when the majority of artist play count lies in the lower interval of the distribution.  ...  To solve this problem, we use the median of the artist play count instead as the statistical object to optimize existing algorithm.  ...  1 and 5 based on the user's listening habit.  ... 
doi:10.12783/dtetr/apetc2017/11361 fatcat:n4tuzt3wq5bz3p2fggpmgbyona

Tag2Risk: Harnessing social music tags for characterizing depression risk

Aayush Surana, Yash Goyal, Manish Shrivastava, Suvi H Saarikallio, Vinoo Alluri
2020 Zenodo  
Mental-well being scores, musical engagement measures, and listening histories of users (N=541) were acquired.  ...  In this age of Big Data, online music streaming services allow us to capture ecologically valid music listening behavior and provide a rich source of information to identify several user-specific aspects  ...  [8] , who have proposed collecting data in more ecologically valid settings, such as user listening histories from music streaming platforms which are a better reflection of the users' true preferences  ... 
doi:10.5281/zenodo.4245450 fatcat:lkhksbz6ebb6dpl56fnxqffmuq

Teenagers, smartphones and digital audio consumption in the age of Spotify
Adolescentes, smartphones y consumo de audio digital en la era de Spotify

Luis Miguel Pedrero-Esteban, Andrés Barrios-Rubio, Virginia Medina-Ávila
2019 Comunicar  
creators need to adapt their value chains to the habits derived from this mediation, especially in the younger audience.  ...  , but also on the user experience.  ...  This is a common consumption habit among teen demographics in Colombia, Mexico and Spain.  ... 
doi:10.3916/c60-2019-10 fatcat:fa4h3wfdmzhori54bk2apdgili

Assessment of Safe Listening Intentional Behavior Toward Personal Listening Devices in Young Adults

Kamakshi V. Gopal, Sara Champlin, Bryce Phillips
2019 International Journal of Environmental Research and Public Health  
to their personal listening devices: (1) lowering the intensity of loud music, and (2) shortening the listening duration of loud music.  ...  Behavioral intentions to turn the music down and listen for shorter durations were thought to be predicted by the TPB factors (attitudes, social norms, and perceived behavioral control).  ...  habits and thus reduce their risk of hearing loss from recreational noise.  ... 
doi:10.3390/ijerph16173180 pmid:31480442 pmcid:PMC6747380 fatcat:jensvjdbozhajgcgj6yxs7k4ju

Social Complex Contagion in Music Listenership: A Natural Experiment with 1.3 Million Participants [article]

John Ternovski, Taha Yasseri
2017 arXiv   pre-print
We generate a novel dataset from, a music tracking website, to analyse the listenership history of 1.3 million users over a two-month time horizon.  ...  Moreover, we show that this effect is contagious and can spread to users who did not attend the event. However, the extent of contagion depends on the type of artist.  ...  A user does not have to listen to the music directly from the website for it to be recorded (or "scrobbled") in the user's track history-a user needs only to install the Audioscrobbler plugin on  ... 
arXiv:1711.05701v1 fatcat:cpbm532xvrbjlozqs7ai2em7b4
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