Data Musicalization
Permalink
Citation
Tulilaulu , A , Nelimarkka , M , Paalasmaa , J , Johnson , D , Ventura , D , Myllys , P & Toivonen , H 2018 , ' Data Musicalization ' , ACM Transactions on Multimedia Computing Communications and Applications , vol. 14 , no. 2 , 47 . https://doi.org/10.1145/3184742
| Title: | Data Musicalization |
| Author: | Tulilaulu, Aurora; Nelimarkka, Matti; Paalasmaa, Joonas; Johnson, Daniel; Ventura, Dan; Myllys, Petri; Toivonen, Hannu |
| Contributor organization: | Department of Computer Science Finnish Centre of Excellence in Algorithmic Data Analysis Research (Algodan) Helsinki Institute for Information Technology Discovery Research Group/Prof. Hannu Toivonen |
| Date: | 2018-05 |
| Language: | eng |
| Number of pages: | 27 |
| Belongs to series: | ACM Transactions on Multimedia Computing Communications and Applications |
| ISSN: | 1551-6857 |
| DOI: | https://doi.org/10.1145/3184742 |
| URI: | http://hdl.handle.net/10138/237053 |
| Abstract: | Data musicalization is the process of automatically composing music based on given data, as an approach to perceptualizing information artistically. The aim of data musicalization is to evoke subjective experiences in relation to the information, rather than merely to convey unemotional information objectively. This paper is written as a tutorial for readers interested in data musicalization. We start by providing a systematic characterization of musicalization approaches, based on their inputs, methods and outputs. We then illustrate data musicalization techniques with examples from several applications: one that perceptualizes physical sleep data as music, several that artistically compose music inspired by the sleep data, one that musicalizes on-line chat conversations to provide a perceptualization of liveliness of a discussion, and one that uses musicalization in a game-like mobile application that allows its users to produce music. We additionally provide a number of electronic samples of music produced by the different musicalization applications. |
| Subject: |
113 Computer and information sciences
Computational Creativity Artificial Intelligence Multimedia Automated Composision Data Analysis Data Science 6131 Theatre, dance, music, other performing arts Music Algorithmic Composition Data musicalization sonification automated composition music data analysis computational creativity WALKING STYLES SONIFICATION |
| Peer reviewed: | Yes |
| Usage restriction: | openAccess |
| Self-archived version: | acceptedVersion |
Files in this item
Total number of downloads: Loading...
| Files | Size | Format | View |
|---|---|---|---|
| data_musicalization_11.pdf | 1.101Mb |
View/ |
This item appears in the following Collection(s)
-
Articles from TUHAT system
[53185]
Related items
Showing items related by title, author, creator and subject.
-
(Helsinki Collegium for Advanced Studies, 2016)COLLeGIUM: Studies across Disciplines in the Humanities and Social Sciences ; 21Following the UK-based model of The Associated Board of the Royal Schools of Music (ABRSM), in 1993 the Central Conservatory of Music (CCOM) in Beijing initiated a similar system called the Standard Grade Examinations in Music (SGEM). Since then, throughout China, all conservatories as well as the Chinese Musicians Association, the Chinese National Orchestration Society and some local music organizations have launched similar examinations, establishing a significant trend in music education. A Grade Exams Center (GEC), which houses the SGEM, is not confined to exams, but also runs related courses in music instrument performance, and organizes relevant publications. This article focuses on the commercial aspects of the SGEM. It classifies four business categories that form a music-industrial chain, and discusses the chain’s benefits and issues for CCOM and Chinese society.
-
-
(2021)Background and objectives Music has a unique capacity to evoke both strong emotions and vivid autobiographical memories. Previous music information retrieval (MIR) studies have shown that the emotional experience of music is influenced by a combination of musical features, including tonal, rhythmic, and loudness features. Here, our aim was to explore the relationship between music-evoked emotions and music-evoked memories and how musical features (derived with MIR) can predict them both. Methods Healthy older adults (N = 113, age ≥ 60 years) participated in a listening task in which they rated a total of 140 song excerpts comprising folk songs and popular songs from 1950s to 1980s on five domains measuring the emotional (valence, arousal, emotional intensity) and memory (familiarity, autobiographical salience) experience of the songs. A set of 24 musical features were extracted from the songs using computational MIR methods. Principal component analyses were applied to reduce multicollinearity, resulting in six core musical components, which were then used to predict the behavioural ratings in multiple regression analyses. Results All correlations between behavioural ratings were positive and ranged from moderate to very high (r = 0.46–0.92). Emotional intensity showed the highest correlation to both autobiographical salience and familiarity. In the MIR data, three musical components measuring salience of the musical pulse (Pulse strength), relative strength of high harmonics (Brightness), and fluctuation in the frequencies between 200–800 Hz (Low-mid) predicted both music-evoked emotions and memories. Emotional intensity (and valence to a lesser extent) mediated the predictive effect of the musical components on music-evoked memories. Conclusions The results suggest that music-evoked emotions are strongly related to music-evoked memories in healthy older adults and that both music-evoked emotions and memories are predicted by the same core musical features.