Algorithmic music for therapy : effectiveness and perspectives

Raglio, Alfredo (author), Baiardi, Paola (author), Vizzari, Giuseppe (author), Imbriani, Marcello (author), Castelli, Mauro (author), Manzoni, Sara (author), Vico, Francisco (author), Manzoni, Luca (author)

This study assessed the short-term effects of conventional (i.e., human-composed) and algorithmic music on the relaxation level. It also investigated whether algorithmic compositions are perceived as music and are distinguishable from human-composed music. Three hundred twenty healthy volunteers were recruited and randomly allocated to two groups where they listened to either their preferred music or algorithmic music. Another 179 healthy subjects were allocated to four listening groups that respectively listened to: music composed and performed by a human, music composed by a human and performed by a machine; music composed by a machine and performed by a human, music composed and performed by a machine. In the first experiment, participants underwent one of the two music listening conditions-preferred or algorithmic music' in a comfortable state. In the second one, participants were asked to evaluate, through an online questionnaire, the musical excerpts they listened to. The Visual Analogue Scale was used to evaluate their relaxation levels before and after the music listening experience. Other outcomes were evaluated through the responses to the questionnaire. The relaxation level obtained with the music created by the algorithms is comparable to the one achieved with preferred music. Statistical analysis shows that the relaxation level is not affected by the composer, the performer, or the existence of musical training. On the other hand, the perceived effect is related to the performer. Finally, music composed by an algorithm and performed by a human is not distinguishable from that composed by a human.

• Language: English
• Keywords: algorithmic music, human/machine composition, Melomics-Health, relaxation, therapeutic music listening
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: EF - School of Economics and Business
• Publication status: Published
• Publication version: Version of Record
• Year: 2021
• Number of pages: 13 str.
• Numbering: Vol. 11, iss. 19 (art. 8833)
• PID: 20.500.12556/RUL-132221
• UDC: 004
• ISSN on article: 2076-3417
• DOI: 10.3390/app11198833
• COBISS.SI-ID: 78036739
• Publication date in RUL: 18.10.2021

Record is a part of a journal

• Title: Applied sciences
• Shortened title: Appl. sci.
• Publisher: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 23.09.2021

Projects

• Funder: FCT - Fundação para a Ciência e a Tecnologia, I.P.
• Project number: DSAIPA/DS/0022/2018
• Name: GADgET

• Funder: ARRS - Slovenian Research Agency
• Funding programme: Raziskovalni program
• Project number: P5-0410
• Name: Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe