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Advances in Music Informatics
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Advances in Music Informatics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 6246

Special Issue Editors

Prof. Dr. Adriano Baratè

E-Mail Website
Guest Editor
Department of Computer Science, Università degli Studi di Milano, 20122 Milano, Italy
Interests: sound and music computing; computational musicology
Prof. Dr. Goffredo Haus

E-Mail Website
Guest Editor
Department of Computer Science, Università degli Studi di Milano, 20122 Milano, Italy
Interests: sound and music computing; multimedia; human–computer interaction; cultural heritage

Special Issue Information

Dear Colleagues,

We are inviting submissions to a Special Issue on Advances in Music Informatics. Both theoretical and experimental works are welcome.

Contributions can focus on problems in symbolic music processing, the implementation and application of new music technologies for education, automatic music information processing, technologies for musical heritage, and other fields of interest in music informatics.

Prof. Dr. Adriano Baratè 
Prof. Dr. Goffredo Haus
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computer-aided composition
  • musical analysis
  • multilevel music representation
  • algorithms and systems for music composition
  • music in education
  • computational musicology
  • educational music tools
  • music information retrieval
  • music for games
  • technologies for musical heritage

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

17 pages, 536 KiB  
Article
Detecting Music-Induced Emotion Based on Acoustic Analysis and Physiological Sensing: A Multimodal Approach
by Xiao Hu, Fanjie Li and Ruilun Liu
Appl. Sci. 2022, 12(18), 9354; https://doi.org/10.3390/app12189354 - 18 Sep 2022
Cited by 1 | Viewed by 3055
Abstract
The subjectivity of listeners’ emotional responses to music is at the crux of optimizing emotion-aware music recommendation. To address this challenge, we constructed a new multimodal dataset (“HKU956”) with aligned peripheral physiological signals (i.e., heart rate, skin conductance, blood volume pulse, skin temperature) [...] Read more.
The subjectivity of listeners’ emotional responses to music is at the crux of optimizing emotion-aware music recommendation. To address this challenge, we constructed a new multimodal dataset (“HKU956”) with aligned peripheral physiological signals (i.e., heart rate, skin conductance, blood volume pulse, skin temperature) and self-reported emotion collected from 30 participants, as well as original audio of 956 music pieces listened to by the participants. A comprehensive set of features was extracted from physiological signals using methods in physiological computing. This study then compared performances of three feature sets (i.e., acoustic, physiological, and combined) on the task of classifying music-induced emotion. Moreover, the classifiers were also trained on subgroups of users with different Big-Five personality traits for further customized modeling. The results reveal that (1) physiological features contribute to improving performance on valence classification with statistical significance; (2) classification models built for users in different personality groups could sometimes further improve arousal prediction; and (3) the multimodal classifier outperformed single-modality ones on valence classification for most user groups. This study contributes to designing music retrieval systems which incorporate user physiological data and model listeners’ emotional responses to music in a customized manner. Full article
(This article belongs to the Special Issue Advances in Music Informatics)
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20 pages, 809 KiB  
Article
Bell Shape Embodying Zhongyong: The Pitch Histogram of Traditional Chinese Anhemitonic Pentatonic Folk Songs
by Hui Liu, Kun Jiang, Hugo Gamboa, Tingting Xue and Tanja Schultz
Appl. Sci. 2022, 12(16), 8343; https://doi.org/10.3390/app12168343 - 20 Aug 2022
Cited by 19 | Viewed by 2387
Abstract
As an essential subset of Chinese music, traditional Chinese folk songs frequently apply the anhemitonic pentatonic scale. In music education and demonstration, the Chinese anhemitonic pentatonic mode is usually introduced theoretically, supplemented by music appreciation, and a non-Chinese-speaking audience often lacks a perceptual [...] Read more.
As an essential subset of Chinese music, traditional Chinese folk songs frequently apply the anhemitonic pentatonic scale. In music education and demonstration, the Chinese anhemitonic pentatonic mode is usually introduced theoretically, supplemented by music appreciation, and a non-Chinese-speaking audience often lacks a perceptual understanding. We discovered that traditional Chinese anhemitonic pentatonic folk songs could be identified intuitively according to their distinctive bell-shaped pitch distribution in different types of pitch histograms, reflecting the Chinese characteristics of Zhongyong (the doctrine of the mean). Applying pitch distribution to the demonstration of the Chinese anhemitonic pentatonic folk songs, exemplified by a considerable number of instances, allows the audience to understand the culture behind the music from a new perspective by creating an auditory and visual association. We have also made preliminary attempts to feature and model the observations and implemented pilot classifiers to provide references for machine learning in music information retrieval (MIR). To the best of our knowledge, this article is the first MIR study to use various pitch histograms on traditional Chinese anhemitonic pentatonic folk songs, demonstrating that, based on cultural understanding, lightweight statistical approaches can progress cultural diversity in music education, computational musicology, and MIR. Full article
(This article belongs to the Special Issue Advances in Music Informatics)
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