Special Issue "Between East and West: Sound and Music Computing Aspects--Selected Paper from SMC-18 and 'Computational Ethnomusicology' Summer School"

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

Deadline for manuscript submissions: closed (30 July 2019).

Special Issue Editors

Assoc. Prof. Anastasia Georgaki
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Guest Editor
Associate Professor, National and Kapodistrian University of Athens, Greece
Assoc. Prof. George Tzanetakis
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Guest Editor
Associate Professor, University of Viktoria, Canada
Assist. Prof. Areti Andreopoulou
E-Mail Website
Guest Editor
Assistant Professor, , National and Kapodistrina University of Athens

Special Issue Information

Dear Colleagues,

In this issue we  investigate computional approaches through signal processing,  encoding, machine learning and other techniques  that deal with different aspects of  Music and Musicology  between East and West.  The starting  point of this investigation emerges from the Archeomusicological aspects  of  music notation, findings and theory, Byzantine Musicology, Cultural musicology, and Ethnomusiology. Under this prism, this special issue will explore the use of novel signal processing and (ethno)musicological tools for the systematic reearch, analysis, and transcription of non-western music civilizations, the analysis and automatic recognition of birdsongs and its relationship to cultural and biological aspects of music, as also the development of assistive tools for composers wishing to interact artistically with unfamiliar music and lost cultures. Last, we will examine the performative aspects of transcultural improvisation through mapping and AI algorithms, automatic music generation, and accompaniment systems.

Assoc. Prof. Anastasia Georgaki
Assoc. Prof. George Tzanetakis
Assist. Prof. Areti Andreopoulou
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 papers will be 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 2000 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

  • Signal processing in non-western music
  • Machine learning in non western music
  • Encoding Ancient music and Byzantine notation
  • Birdsong automatic transcription
  • Computational Ethnomuciology
  • Improvisation mapping

Published Papers (1 paper)

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Research

Open AccessArticle
Automatic Segmentation of Ethnomusicological Field Recordings
Appl. Sci. 2019, 9(3), 439; https://doi.org/10.3390/app9030439 - 28 Jan 2019
Cited by 1 | Viewed by 1138
Abstract
The article presents a method for segmentation of ethnomusicological field recordings. Field recordings are integral documents of folk music performances captured in the field, and typically contain performances, intertwined with interviews and commentaries. As these are live recordings, captured in non-ideal conditions, they [...] Read more.
The article presents a method for segmentation of ethnomusicological field recordings. Field recordings are integral documents of folk music performances captured in the field, and typically contain performances, intertwined with interviews and commentaries. As these are live recordings, captured in non-ideal conditions, they usually contain significant background noise. We present a segmentation method that segments field recordings into individual units labelled as speech, solo singing, choir singing, and instrumentals. Classification is based on convolutional deep networks, and is augmented with a probabilistic approach for segmentation. We describe the dataset gathered for the task and the tools developed for gathering the reference annotations. We outline a deep network architecture based on residual modules for labelling short audio segments and compare it to the more standard feature based approaches, where an improvement in classification accuracy of over 10% was obtained. We also present the SeFiRe segmentation tool that incorporates the presented segmentation method. Full article
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