Artificial intelligence – be aware – elevator music does not fit for all comparison between non-musicians and musicians with AI architectural plan

Tekoäly tukee ihmisen toimintaa kasvavalla nopeudella ja monialaisesti. Musiikkialalla, teknologiset ratkaisut ja ohjelmistot kytkeytyvät käyttäjäkokemuksiin monien eri kanavien kautta. Näitä ovat esim. musiikin kuuntelu, saatavuus ja tarjonta, peliala, kuluttajakäyttäytyminen, aivotutkimus, terveys...

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Main Author: Peltoniemi, Janne
Other Authors: Informaatioteknologian tiedekunta, Faculty of Information Technology, Informaatioteknologia, Information Technology, Jyväskylän yliopisto, University of Jyväskylä
Format: Master's thesis
Language:eng
Published: 2023
Subjects:
Online Access: https://jyx.jyu.fi/handle/123456789/87622
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author Peltoniemi, Janne
author2 Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_facet Peltoniemi, Janne Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä Peltoniemi, Janne Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
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description Tekoäly tukee ihmisen toimintaa kasvavalla nopeudella ja monialaisesti. Musiikkialalla, teknologiset ratkaisut ja ohjelmistot kytkeytyvät käyttäjäkokemuksiin monien eri kanavien kautta. Näitä ovat esim. musiikin kuuntelu, saatavuus ja tarjonta, peliala, kuluttajakäyttäytyminen, aivotutkimus, terveysala, musiikin tuottaminen ja esittäminen, musiikin harjoittelu ja musiikin hyödyntäminen ammatillisissa erikoisaloissa (kirurgit, lentäjät). Käyttäjäkokemuksiin sisältyy kuitenkin oletuksia, jotka saattavat olla virheellisiä tai ainakin poikkeavia tekoälyn generoimassa tarjonnassa. Käyttäjäkokemus ilmenee musiikissa hyvin yksilöllisesti. Erityisesti musiikin tuntemukset liittyvät paljolti yksilön omaan taustaan, kokemuksiin ja musiikilliseen ymmärrykseen. Aikaisemmissa tutkimuksissa musiikin laadun ja tuntemusten suhdetta on analysoitu, mutta usein kausaliteetti on jäänyt ohueksi. Tämä voi osaltaan johtua siitä, että musiikkinäytteiden teoreettiset perusteet jäävät heikoiksi ja yhteyttä kuuntelijan musiikilliseen ymmärrykseen ei tunnisteta. Tässä tutkimuksessa on analysoitu 10 musiikillista näytettä, joiden taustalla on teoreettinen perustelu laadusta ja erityisesti musiikillisesta jännitteestä. Näytteet edustavat jazz-musiikkia, jossa värien, tunnelmien ja tilan vaihtelu mahdollistavat analyysin kahden erilaisen ryhmän välillä, muusikkojen ja ei-muusikkojen. Tutkimusmenetelmänä on sovellettu Design science research (DSR) suunnittelututkimusta ja edelleen kokeellista tutkimusta (Experimental research), jossa on yhdistetty kvantitatiivista ja kvalitatiivista tutkimusta triangulaationa. Tulosten perusteella painottuvat kaksi tekijää; (i) musiikin laadun hienojakoisuus ja (ii) kuuntelijan/käyttäjän musiikillinen tausta ja ymmärrys, jotka olisi tunnistettava kohdennetuissa musiikkipalveluissa ja -sovelluksissa. Digitaaliset tekoälyä (AI) hyödyntävät musiikkisovellukset tarjoavat käyttäjilleen usein kappaleita, jotka perustuvat melko heikohkoon yksilökohtaiseen parametrointiin. Lisäksi musiikin sisältämiä hienojakoja, teoreettisia jännitteitä, ei juurikaan hyödynnetä tekoälyn tarjonnassa. Tulosten perusteella on luotu arkkitehtuurinen sovellusmalli tekoälyn tuloksellisempaan käyttämiseen. Musiikilliset ominaisuudet saattavat usein liittyä vahvoihin käyttäjien tuntemuksiin, kuten onnellisuusteen tai ahdistukseen, riippuen käyttäjän musiikillisesta ymmärryksestä. Monissa tapauksissa tekoäly saattaakin erehtyä ehdottamaan käyttäjälle sisältöä, jonka vaikutus ilmenee päinvastaisena oletettuun nähden. Toisin sanoen ja metaforisestikin todettuna, hissimusiikki ei ole ratkaisu AI technology is supporting and assisting humans at in increasing speed and in various ways. In music, a massive number of technological solutions and software applications are involved in the user experience (UX) whether it is listening to music, eGames, shopping behavior, brainwaves, health care, mental orientation, music production and performance, music rehearsing or focus-oriented demands in professional fields (surgeons, flight pilots). Still, big gaps exist in user assumptions, which may be biasing the further utilization of AI technology. User experience in music is a very unique process with individual variances. When reflected in emotions, the individual responses are largely connected to background experiences and skills of a person, e.g. musical competence. Previous literature has been attempting to solve causalities in emotional response regarding certain music qualities. However, studies are unambiguous in their conclusive implications on this, partly due to somewhat weak efforts to analyze, select and produce theoretically firm musical contents supporting the musicianship levels, or the trained ear of the user/listener, to describe it. In this study, 10 audio stimuli were carefully designed and selected by using jazz music as the representative genre. In jazz, the variation of colors, sounds and ambience are in favor of this type of research setting, where two different groups – non-musicians and musicians – are compared in their emotional responses. Design science research (DSR) and specifically experimental research was applied with qualitative and quantitative methods to analyze the differences between fifteen participants. The results contribute to the literature by implicating two important issues; (i) the fine-quality of provided music is an essential factor and (ii) listener’s musical competence needs to be solved to match the offered music content. Based on the results, digital AI app is designed for more efficient matching. Digitalized music services (AI) are massively offering music tracks to listeners based on quite loosely personalized parameters and furthermore, even a non-existent analysis of the tensions, which can arouse very strong extreme emotions, such as overjoy and anxiety – depending on the musical experience or musicianship of the listener. In many cases, present music services may lead to biased suggestions which are opposing the expectations of the listener. In other words, and also metaphorically, elevator music is not the solution.
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Musiikkialalla, teknologiset ratkaisut ja ohjelmistot kytkeytyv\u00e4t k\u00e4ytt\u00e4j\u00e4kokemuksiin\nmonien eri kanavien kautta. N\u00e4it\u00e4 ovat esim. musiikin kuuntelu, saatavuus ja\ntarjonta, peliala, kuluttajak\u00e4ytt\u00e4ytyminen, aivotutkimus, terveysala, musiikin\ntuottaminen ja esitt\u00e4minen, musiikin harjoittelu ja musiikin hy\u00f6dynt\u00e4minen ammatillisissa erikoisaloissa (kirurgit, lent\u00e4j\u00e4t). K\u00e4ytt\u00e4j\u00e4kokemuksiin sis\u00e4ltyy kuitenkin oletuksia, jotka saattavat olla virheellisi\u00e4 tai ainakin poikkeavia teko\u00e4lyn\ngeneroimassa tarjonnassa. K\u00e4ytt\u00e4j\u00e4kokemus ilmenee musiikissa hyvin yksil\u00f6llisesti. Erityisesti musiikin tuntemukset liittyv\u00e4t paljolti yksil\u00f6n omaan taustaan,\nkokemuksiin ja musiikilliseen ymm\u00e4rrykseen. Aikaisemmissa tutkimuksissa\nmusiikin laadun ja tuntemusten suhdetta on analysoitu, mutta usein kausaliteetti\non j\u00e4\u00e4nyt ohueksi. 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spellingShingle Peltoniemi, Janne Artificial intelligence – be aware – elevator music does not fit for all : comparison between non-musicians and musicians with AI architectural plan architecture emotional response tensions Tietojärjestelmätiede Information Systems Science 601 tekoäly musiikki tunteet muusikot kuunteleminen jazz artificial intelligence music emotions musicians listening
title Artificial intelligence – be aware – elevator music does not fit for all : comparison between non-musicians and musicians with AI architectural plan
title_full Artificial intelligence – be aware – elevator music does not fit for all : comparison between non-musicians and musicians with AI architectural plan
title_fullStr Artificial intelligence – be aware – elevator music does not fit for all : comparison between non-musicians and musicians with AI architectural plan Artificial intelligence – be aware – elevator music does not fit for all : comparison between non-musicians and musicians with AI architectural plan
title_full_unstemmed Artificial intelligence – be aware – elevator music does not fit for all : comparison between non-musicians and musicians with AI architectural plan Artificial intelligence – be aware – elevator music does not fit for all : comparison between non-musicians and musicians with AI architectural plan
title_short Artificial intelligence – be aware – elevator music does not fit for all
title_sort artificial intelligence be aware elevator music does not fit for all comparison between non musicians and musicians with ai architectural plan
title_sub comparison between non-musicians and musicians with AI architectural plan
title_txtP Artificial intelligence – be aware – elevator music does not fit for all : comparison between non-musicians and musicians with AI architectural plan
topic architecture emotional response tensions Tietojärjestelmätiede Information Systems Science 601 tekoäly musiikki tunteet muusikot kuunteleminen jazz artificial intelligence music emotions musicians listening
topic_facet 601 Information Systems Science Tietojärjestelmätiede architecture artificial intelligence emotional response emotions jazz kuunteleminen listening music musicians musiikki muusikot tekoäly tensions tunteet
url https://jyx.jyu.fi/handle/123456789/87622 http://www.urn.fi/URN:NBN:fi:jyu-202306123691
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