Warianty tytułu
Języki publikacji
Abstrakty
An online survey was carried out to explore how respondents listen to music recordings. It was anticipated that the listener’s preferences would be influenced by various factors, such as age, music genre, the contexts in which they listen, and their favored methods of music consumption. Consequently, the data were collected to analyze these relationships. The survey, structured as a web application, encompassed 23 questions, with seven specifically aimed at defining the respondents and the remainder contributing to the dataset for analysis. The results reveal a prevailing preference for listening to music via streaming platforms. Respondents predominantly engage in passive listening, where music becomes a background presence without commanding their focused attention. Moreover, the data also highlight a noteworthy correlation between preferred music genres and the age of the listeners.
Słowa kluczowe
Rocznik
Tom
Strony
367--372
Opis fizyczny
Bibliogr. 13 poz., il., wykr.
Twórcy
autor
- Gdansk University of Technology, Faculty of Electronics, Telecommunications and Informatics
autor
- Gdansk University of Technology, Faculty of Electronics, Telecommunications and Informatics
autor
- Gdansk University of Technology, Faculty of Electronics, Telecommunications and Informatics
autor
- Gdansk University of Technology, Faculty of Electronics, Telecommunications and Informatics, bokostek@audioakustyka.org
Bibliografia
- [1] Community “Radio Listener Survey. ” https://www.cbaa.org.au/broadcasters/get-data-national-listener-survey-station-census/national-listener-survey-fact-sheets (accessed on September ‘2023).
- [2] M. Kamalzadeh, D. Baur, and T. Möller, “A survey on music listening and management behaviours,” in Proc. of the 13th International Society for Music Information Retrieval Conference (ISMIR 2012), Porto, Portugal, Oct. 8-12.2012.
- [3] E. Gurgen, “Social and Emotional Function of Musical Listening: Reasons for Listening to Music. Eurasian Journal of Educational Research,” vol. 16, pp. 1-30, 2016. https://doi.org/10.14689/ejer.2016.66.13.
- [4] K. R. Sanfilippo, N. Spiro, Neta, M. Molina-Solana, and A. Lamont, “Do the shuffle: Exploring reasons for music listening through shuffled play,” PLOS ONE, vol. 15, 2, 2020. https://doi.org/e0228457.10.1371/journal.pone.0228457.
- [5] L. Barton, G. Candan, T. Fritz, T. Zimmermann, and G. Murphy, “The Sound of Software Development: Music Listening Among Software Engineers,” IEEE Software, pp. 1-1, 2019. https://doi.org/10.1109/MS.2019.2906312.
- [6] T. Vilkajanen, “Design and implementation of memory forensics automation solution,” Jyväskylä University University of Applied Sciences, B.Sc. thesis, 2021. https://www.theseus.fi/bitstream/handle/10024/339796/Ghimire_Devndra.pdf?sequence=2&isAllowed=y (accessed on Octobe ‘2023)
- [7] A. Butler, “Why Streaming is a Good Thing for the Music Industry,” Backstage Pass, vol. 2, no. 1, article 22, 2019.
- [8] R. Rahimi and K-H. Park, “A Comparative Study of Internet Architecture and Applications of Online Music Streaming Services: The Impact on The Global Music Industry Growth,” pp. 1-6, 2020. https://doi.org/10.1109/ICoICT49345.2020.9166225.
- [9] Y. Song, S. Dixon, and M. Pearce, “A Survey of Music Recommendation Systems and Future Perspectives,” in Proc. 9th International Symposium on Computer Music Modeling and Retrieval (CMMR), 2012.
- [10] J. Grubba, K. Lempkowski, “Exploring machine learning algorithms for music album classification based on their cover,” M.Sc. thesis (supervisor B. Kostek), Gdańsk University of Technology, Faculty of Electronics, Telecommunications and Informatics, Multimedia Systems Department, 2023.
- [11] C. Brooks, “Spotify Wrapped 2023: 'Music genres are now irrelevant to fans',” https://www.bbc.com/news/entertainment-arts-67111517 (accessed on February ‘2024).
- [12] R. Kotsakis, C. Dimoulas, “Extending Radio Broadcasting Semantics through Adaptive Audio Segmentation Automations,” Knowledge. Vol. 2(3), pp. 347-364, 2022. https://doi.org/10.3390/knowledge2030020.
- [13] C. O. B. Weerathunga, K. L. Jayaratne, and P. Gunawardana, “Classification of public radio broadcast context for onset detection,” European Journal of Computer Science and Information Technology, vol. 7, no. 6, pp. 1-22, December 2019.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-d2ed248d-3dde-4402-8d49-db9a26bcdb56