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) | Viewed by 3945

Special Issue Editors


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Guest Editor
Associate Professor, National and Kapodistrian University of Athens, Athens, Greece

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Guest Editor
Associate Professor, University of Viktoria, Canada

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Guest Editor
Assistant Professor, National and Kapodistrina University of Athens, Athens, Greece

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

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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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12 pages, 1146 KiB  
Article
Automatic Segmentation of Ethnomusicological Field Recordings
by Matija Marolt, Ciril Bohak, Alenka Kavčič and Matevž Pesek
Appl. Sci. 2019, 9(3), 439; https://doi.org/10.3390/app9030439 - 28 Jan 2019
Cited by 6 | Viewed by 3522
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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