Cognition Technique for Developing a World Music
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33087
Cognition Technique for Developing a World Music

Authors: Haider Javed Uppal, Javed Yunas Uppal

Abstract:

In today's globalized world, it is necessary to develop a form of music that is able to evoke equal emotional responses among people from diverse cultural backgrounds. Indigenous cultures throughout history have developed their own music cognition, specifically in terms of the connections between music and mood. With the advancements in artificial intelligence technologies, it has become possible to analyze and categorize music features such as timbre, harmony, melody, and rhythm, and relate them to the resulting mood effects experienced by listeners. This paper presents a model that utilizes a screenshot translator to convert music from different origins into waveforms, which are then analyzed using machine learning and information retrieval techniques. By connecting these waveforms with Thayer's matrix of moods, a mood classifier has been developed using fuzzy logic algorithms to determine the emotional impact of different types of music on listeners from various cultures.

Keywords: Cognition, world music, artificial intelligence, Thayer’s matrix.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 147

References:


[1] Nori Jacoby, Elizabeth Hellmuth Margulis, Martin Clayton and Erin Hannon. Cross-Cultural Work in Music Cognition: Challenges, Insights, and Recommendations. February 2020. Music Perception 37(3):185-195. DOI:10.1525/mp.2020.37.3.185 https://www.researchgate.net/publication/339378902_Cross-Cultural_ Work_in_Music_Cognition_Challenges_Insights_and_Recommendations
[2] Ian Cross, Music, Cognition, Culture, and Evolution, 2006, Annals of the New York Academy of Sciences, Faculty of Music, University of Cambridge, Cambridge CB3 9DP, United Kingdom, https://www.academia.edu/11142909/Music_Cognition_Culture_and_Evolution
[3] Cross-cultural Similarities and Differences in Music Mood Perception, Jin Ha Lee, Xiao Hu. iConference 2014. Proceedings (p. 259–269). doi:10.9776/14081. University of Washington, Information School, University of Hong Kong. 2014. https://bpb-us-e1.wpmucdn.com/sites.uw.edu/dist/2/3760/files/2019/09/Cross-cultural-similarities-and-differences-in-music-mood-perception.pdf
[4] Parul Agarwal, Harish Karnick, Bhiksha Raj. A Comparative Study of Indian and Western Music Forms. Indian Institute of Technology, Kanpur, India, and Carnegie Mellon University, USA. International Society for Music Information Retrieval. Ismir 2013. https://archives.ismir.net/ismir2013/paper/000027.pdf
[5] Jacopo Conti. Popular Music Analysis and Semiotics: Applications and Perspectives. DOI 10.21697/zk.2020.7.0. Dipartimento di Filosofia e Scienze, Università di Torino, Department of Philosophy and Educational Sciences, University of Turin, Italy. ORCID: 0000-0003-0032-5545 https://www.academia.edu/44867869/Popular_Music_Analysis_and_Semiotics_Applications_and_Perspectives
[6] Carlos N. Silla, Alessandro L. Koerich & Celso A. A. Kaestner. A Machine Learning Approach to Automatic Music Genre Classification. Journal of the Brazilian Computer Society volume 14, pages7–18 (2008). https://journal-bcs.springeropen.com/articles/10.1007/BF03192561
[7] Professor Barbara Hesser, New York University, et al., ‘Music as a Global Resource: Solutions for Social and Economic Issues Compendium - Third Edition’, Age of Connectivity: Cities, Magnets of Hope A Contribution in Support of the Millennium Development Goals, Habitat Agenda and the United Nations Sixth World Urban Forum, “The Urban Future” Fall 2011 United Nations Headquarters. http://international-iccc.org/wp-content/uploads/2015/02/MAGR_FINAL_2011.pdf
[8] Gregory H. Bontrager, ‘Breaking Free of the Language Barrier in Music’, Translation Journal, Vol 15, No 3, July 2011. http://translationjournal.net/journal/57lyrics.htm
[9] Krystoof Kubacki and Robin Croft, ‘Artists' attitudes to marketing: a cross-cultural perspective’ International Journal of Nonprofit and Voluntary Sector Marketing, Volume 11, Issue 4, November 2006, Pages 335–345, http://onlinelibrary.wiley.com/doi/10.1002/nvsm.287/full
[10] Igor Vatolkin and Wolfgang Theimer, ‘Introduction to Methods for Music Classification Based on Audio Data’, Nokia, Research Center, NRC-TR-2007-012. http://citeseerx.ist.psu.edu/viewdoc/download?rep=rep1&type=pdf&doi=10.1.1.71.8321
[11] Michael Haggblade, Yang Hong, and Kenny Kao, ‘Music Genre Classification’, CS229 Project Report, Machine Learning Autumn 2012, Stanford University. http://cs229.stanford.edu/proj2011/HaggbladeHongKao-MusicGenreClassification.pdf
[12] Jim Patterson,’ What is Music’, Myfiles Ltd, website, Scotland, UK, 2017 https://www.mfiles.co.uk/what-is-music.htm
[13] Cyril Lauriel, ‘Automatic Classification of Musical Mood by Content Based Analysis’, PhD Thesis submitted to Pompeu Fabra University, Music Technology Group, Barcelona, Spain, 2011.
[14] Jose Padial, Ashish Goe,‘Music Mood Classification’, CS 229 Project Report, Machine Learning Autumn 2011, Stanford University. http://cs229.stanford.edu/proj2011/GoelPadial-MusicMoodClassification.pdf
[15] Franzon, Johan. (2014). Choices in Song Translation. The Translator. 14. 373-399. 10.1080/13556509.2008.10799263. https://www.researchgate.net/publication/261668226_Choices_in_Song_Translation
[16] Şebnem Susam-Sarajeva, ‘Translation and Music: Changing Perspectives, Frameworks and Significance’, The Translator: Vol 14, No 2 Pages 187-200, University of Edinburgh, Scotland, 21 Feb 2014, http://www.tandfonline.com/doi/abs/10.1080/13556509.2008.10799255?journalCode=rtrn20
[17] Lucile Desblache, ‘Translating Music’, Centre for Research in Translation and Transcultural Studies, University of Roehampton, London, 2016. http://translatingmusic.com/styled-6/index.html
[18] Oliver Kucharovic, ‘Music Translator (Recognition)’, Rolníckej Skoly, Komárno Slovakia, 2017. https://www.allfreeapk.com/music-translator-recognition,13331816/
[19] Chia-Chu Liu, Yi-Hsuan Yang, Ping-Hao Wu, and Homer H. Chen, ‘Detecting and Classifying Emotion in Popular Music’, Graduate Institute of Communication Engineering, National Taiwan University, 2003. seerx.ist.psu.edu/viewdoc/download?doi=10.1.1.458.3978&rep=rep1&type=pdf
[20] Thompson, W.F. & Balkwill, L-L. ‘Cross-cultural similarities and differences’. Patrik Juslin and John Sloboda (Eds.), Handbook of Music and Emotion: Theory, Research, Applications, Chapter 27 (pp. 755-788). Oxford University Press, 2010. https://www.researchgate.net/publication/275461754_Cross-cultural_similarities_and_differences_Music_and_Emotion
[21] Aniruddha M. Ujlambkar, ‘Mood classification of Indian popular music’, Proceedings of the CUBE International Information Technology Conference, Pages 278-283, Pune, India, Sep 03-05, 2012. http://dl.acm.org/citation.cfm?id=2381768
[22] Racy, Ali Jihad. Making Music in the Arab World: The Culture and Artistry of Ṭarab. Publisher: Cambridge; New York: Cambridge University Press, 2003. ISBN 0-521-30414-8. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://assets.cambridge.org/97805213/16859/frontmatter/9780521316859_frontmatter.pdf
[23] Lee, J. H., & Hu, X., ‘Cross-cultural Similarities and Differences in Music Mood Perception’. In iConference 2014 Proceedings (p. 259–269) 2014. doi:10.9776/14081. https://www.ideals.illinois.edu/bitstream/handle/2142/47304/081_ready.pdf?sequence=2
[24] Hong, Y. Euny "In the Arab World, Pop Stardom Can Be A Touchy Subject article", Washington Post, (2005-06-03) http://www.classicalarabicmusic.com/Um%20kalthoum%20Notations%20samples/02-Alatlal.htm
[25] Wikipedia, ‘List of Most Viewed YouTube Videos’, Website, 2017. https://en.m.wikipedia.org/wiki/List_of_most_viewed_YouTube_videos
[26] Jared Anderson, ‘Music Notes’, National Association for Music Education, Website, Madison WI, 2017. https://www.musicnotes.com/search/go?w=movie/tv&from=header
[27] World Chart, ‘Top 50 Most Viewed Arabic Songs on YouTube’, Statoz Arabic, Website, New York, Sep 24, 2017. http://www.allthelyrics.com/forum/showthread.php?t=129778
[28] Ranveer Singh, ‘With 234 million views, guess which is the most watched Hindi song on YouTube ever’, Hindustan Times, eMagazine, New Delhi, India, Sep 24, 2017. http://www.hindustantimes.com/music/with-234-million-views-guess-which-is-the-most-watched-hindi-song-on-youtube-ever/story-g2hIag5rJw8YEbYUSYbjSM.html