pink edition.
Denis Knauf
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!!! 5
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%html(lang='en')
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%head
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-#%meta(charset="utf-8")
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%title “Düsterkeit” in der Musik: Physikalische Entsprechungen und Vorhersagemodelle
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%title Decoding the sound of 'hardness' and 'darkness' as perceptual dimensions of music
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-#%link(rel="stylesheet" href="fonts/Roboto.css")
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%body
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%header(style="")
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%figure.logos(style="margin-top:0.3cm")<>
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%img#uni-logo(src="files/Uni_Logo_2016_ausschnitt.gif")
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%img#tagungs-logo(style="float:right" src="files/daga-2018-logo.png")
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%div(style="font-size:0.8em;margin-top:1.31cm")
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%img#tagungs-logo(style="float:right" src="files/icmpc15_logo.jpg")
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%img#uni-logo(src="files/univie_logo.png")
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-#%div(style="font-size:0.8em;margin-top:1.31cm")
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44. Jahrestagung für Akustik
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%br<>
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Technische Universität München
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@ -52,103 +52,230 @@
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.grabstein-wo Technische Universität München
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.grabstein-von ✦ 19. März 2018
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.grabstein-bis ✝ 22. März 2018
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%img(style="height:7cm;top:3cm;right:24cm;position:absolute" alt="Dunkle Nacht" src="files/Candle.png")
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-#%img(style="height:7cm;top:3cm;right:24cm;position:absolute" alt="Dunkle Nacht" src="files/Candle.png")
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%h1
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<q>Düsterkeit</q> in der Musik:
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-#%br<>
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Physikalische Entsprechungen und Vorhersagemodelle
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Decoding the sound of <q>hardness</q> and <q>darkness</q> as perceptual dimensions of music
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%p#authors<>
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%span.author(data-mark="1,2")<> Isabella Czedik-Eysenberg
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%span.author(data-mark="1")<> Christoph Reuter
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%span.author(data-mark="2")<> Denis Knauf
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%p#institutions<>
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%span.institution(data-mark="1")<> Institut für Musikwissenschaft, Universität Wien
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%span.institution(data-mark="2")<> Informatik, Technische Universität Wien
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%span.institution(data-mark="1")<> University of Vienna, Austria
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%span.institution(data-mark="2")<> Student at Technical University of Vienna, Austria
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%main
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#column1
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%section#hintergrund
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%h1 1. Hintergrund
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%p
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Von Dowlands <q>Songs of Darkness</q> bis hin zu Genres wie Gothic oder
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Doom Metal ist <q>Düsterkeit</q> eine Dimension von Musik, die sich auch
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abseits melan-<jbr/>cholischer Texte im Klangbild niederschlagen kann.
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%p
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In musikalischen Adjektiv-Klassifikationen bildete der Begriff
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<q>düster</q> (<q>dark</q>) einen gemeinsamen Cluster mit Trauer-bezogenen
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Adjektiven (etwa <q>gloomy</q>, <q>sad</q>, <q>depressing</q>)
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#{quellen Hevner: 1936}.
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Jene Verknüpfung wurde auch in späteren Arbeiten zur automatisierten
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musikalischen Affektdetektion aufgegriffen #{quellen 'Li & Ogihara'=>2003}.
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Dies würde die Relevanz von Klangattributen,
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welche mit dem Ausdruck von Trauer assoziiert sind – etwa
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Moll-Tonarten oder auch tiefere Grundtonhöhen,
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geringe Melodiebewegungen und ein <q>düsteres</q> Timbre
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#{quellen Huron: 2008} – nahelegen.
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%p
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Von der Klangfarbenforschung ausgehend wird <q>Helligkeit</q> (messbar
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an der spektralen Verteilung eines Klanges) häufig als eine zentrale
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perzeptuelle Klangfarbendimension angesehen
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#{quellen 'Wedin & Goude'=>1972, Bismarck: 1974, Grey: 1975, 'Siddiq et al.'=>2014}.
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Dies wirft u.a. die Frage auf, ob es hierbei einen (umgekehrt
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proportionalen) Zusammenhang gibt, bzw. welche anderen
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zusätzlichen Faktoren zu einer klanglichen Düsterkeitsbewertung
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beitragen.
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#column1_1
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%section#heavy_features
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:markdown
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Sound Features
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==============
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%section#fragestellung
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%h1 2. Fragestellungen und Ziele
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Considering Bonferroni correction, 65 significant feature
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correlations were found for the concept of <q>hardness</q>.
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The characterizing attributes of <q>hardness</q> include high
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tempo and sound density, less focus on clear melodic lines than
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noise-like sounds and especially the occurrence of strong percussive
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components.
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%ol
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%li
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%p
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Wie lässt sich das Wahrnehmungskonzept klanglicher <q>Düsterkeit</q>
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anhand von Audiomerkmalen charakterisieren?
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percussive energy / rhythmic density
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%figure
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%img(style="width:50%" src="files/sonagramm_blunt_log.png")
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%img(style="width:50%" src="files/sonagramm_decap_log.png")
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%li
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%p
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Korreliert die empfundene klangliche <q>Düsterkeit</q> umgekehrt
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proportional mit klangfarblicher <q>Helligkeit</q>?
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dynamic distribution
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%figure
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%img(style="width:50%" src="files/blunt_envelope.png")
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%img(style="width:50%" src="files/decap_envelope.png")
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%figure
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%img(style="width:50%" src="files/blunt_dyndist.png")
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%img(style="width:50%" src="files/decap_dyndist.png")
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%li
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%p
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Ziel ist die Erstellung eines Modells zur automatischen Vorhersage
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der wahrgenommenen <q>Düsterkeit</q> von Musikstücken.
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melodic content / harmonic entropy
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%figure
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%img(style="width:50%" src="files/blunt_chromagram.png")
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%img(style="width:50%" src="files/decap_chromagram.png")
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%section#heavy_model
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%h1 Model
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:markdown
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Sequential feature selection
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%section#methoden
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%h1 3. Methoden
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%p
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150 Musikbeispiele aus 10 unterschiedlichen Subgenres der Bereiche
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Metal, Techno, Gothic und Pop wurden anhand von Hörerstatistiken
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der Internetplattform LastFM selektiert. Die Refrains dieser
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Stücke wurden 40 Versuchspersonen zur Bewertung dargeboten, um
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eine Ground Truth für die Wahrnehmung der <q>Düsterkeit</q> von
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Musikbeispielen zu erheben. Unter Einsatz von Essentia
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#{quellen 'Bogdanov et al.'=> 2013}, MIRtoolbox
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#{quellen 'Lartillot & Toiviainen'=> 2007},
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Loudness Toolbox #{quellen Genesis: 2009} und TSM Toolbox
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#{quellen 'Driedger & Müller'=>2014} wurden 230
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Signaleigenschaften über die spektrale Verteilung, zeitliche und
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dynamische Klangfaktoren gewonnen. Diese wurden mittels Machine
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Learning-Verfahren unter Kreuzvalidierung ausgewertet und kombiniert,
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um auf Basis der Hörversuchsdaten Vorhersagemodelle zu erstellen.
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* set of 5 features
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* predictive linear regression model
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%section#schlussfolgerungen
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-#(style="width:44.5%;display:inline-block;float:left")
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%h1 5. Diskussion/Schlussfolgerungen
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%p
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Es konnte anhand von Audiomerkmalen ein Modell zur Vorhersage
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einer Bewertung musikalischer Düsterkeit aufgestellt werden
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<nobr>(Korrelation r = 0,80, p < 0,01)</nobr>.
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%p
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Eine Antiproportionalität mit klangfarblicher Helligkeit ließ sich
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mit gängigen Maßen hierfür nicht nachweisen.
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%p
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Eine Gleichsetzung mit <q>traurig</q> scheint nicht 1:1 möglich zu sein,
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sehr wohl finden sich aber starke Zusammenhänge (etwa Moll-Harmonik).
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%p
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Das Konzept soll in folgenden Untersuchungen genauer in Hinblick
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auf das Interrater-Agreement und den Zusammenhang zu anderen
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Wahrnehmungskonzepten betrachtet werden.
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RMSE | 0.64
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R-Squared | 0.80
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MSE | 0.40
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MAE | 0.49
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r | 0.900
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%figure
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%img(src="scatter_hardness_model5.png")
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%section#heavy_rater_agreement
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:markdown
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Rater Agreement
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===============
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#column2
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%section#ergebnisse1(style="height:96.35cm")
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Intraclass Correlation Coefficient (Two-Way Model, Consistency): <b>0.653</b>
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#column1_2
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-#%section#aims
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%h1 Aims
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%p
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Based on computationally obtainable signal features, the creation
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of models for the perceptual concepts of <q>hardness</q> and
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<q>darkness</q> in music is aimed for. Furthermore it shall be
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explored if there are interactions between the two factors and to
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which extent it is possible to classify musical genres based on
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these dimensions.
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%section#method
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%h1 Method
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%figure.right(style="width:50%")
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%img(src="files/LastFM.png")
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:markdown
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Based on last.fm listener statistics, 150 pieces of music were selected
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from 10 different subgenres of metal, techno, gothic and pop music.
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In an online listening test, 40 participants were asked to rate the
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refrain of each example in terms of <q>hardness</q> and <q>darkness</q>.
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These ratings served as a ground truth for examining the two
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concepts using a machine learning approach:
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Taking into account 230 features describing spectral distribution,
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temporal and dynamic properties, relevant dimensions were
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investigated and combined into models.
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Predictors were trained using five-fold cross-validation.
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%figure.right(style="width:50%")
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%img(src="files/einhorn/diagramm_vorgang_english.png")
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%section#data
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%h1 Data
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%figure.right(style="width:50%")
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%img(src="files/scatter_hard_dark_dashedline_2017-09-05.png")
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%section#hardness
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%h1 Hardness
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%p
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<q>Hardness</q> is often considered a distinctive feature of (heavy)
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metal music, as well as in genres like hardcore techno or <q>Neue
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Deutsche Härte</q>.
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In a previous investigation the concept of <q>hardness</q> in music
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was examined in terms of its acoustic correlates and suitability as
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a descriptor for music #{quellen 'Czedik-Eysenberg et al.' => 2017}.
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#column1_3
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%section#darkness
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%h1 Darkness
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%p
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Certain kinds of music are sometimes described as <q>dark</q> in a
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metaphorical sense, especially in genres like gothic or doom metal.
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According to musical adjective classifications <q>dark</q> is part
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of the same cluster as <q>gloomy</q>, <q>sad</q> or
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<q>depressing</q> #{quellen Hevner: 1936}, which was later adopted in
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computational musical affect detection
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#{quellen 'Li & Oghihara' => 2003}.
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This would suggest the
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relevance of sound attributes that correspond with the expression
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of sadness, e.g. lower pitch, small pitch movement and <q>dark</q>
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timbre #{quellen Huron: 2008}. In timbre research <q>brightness</q>
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is often considered one of the central perceptual axes
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#{quellen Grey: 1975, 'Siddiq et al.' => 2014}, which raises the
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question if <q>darkness</q> in music is also reflected as the
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inverse of this timbral <q>brightness</q> concept.
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%section#darkness_features
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:markdown
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Sound Features
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==============
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Considering Bonferroni correction, 35 significant feature
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correlations were found for the <q>darkness</q> ratings.
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While a suspected negative correlation with **timbral
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<q>brightness</q>** cannot be confirmed, <q>darkness</q> appears to
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be associated with a high **spectral complexity** and harmonic
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traits like **major or minor mode**.
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%figure
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%img(src="files/scatter_spectral_centroid_essentia_darkness.png")
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:markdown
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Correlations between darkness rating and measures for brightness:
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Feature | r | p
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-----------------------|--------|----------
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Spectral centroid | 0.3340 | <0.01
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High frequency content | 0.1526 | 0.0631
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%figure
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%img(src="files/violin_keyEdma_darkMean_blaugelb.png")
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%p
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Musical excerpts in minor mode were significantly rated as
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<q>harder</q> than those in major mode. (<nobr>p < 0.01</nobr>
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according to t-test)
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%section#darkness_model
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%h1 Model
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%figure
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%img(src="files/scatter_darkness_model8.png")
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:markdown
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Sequential feature selection:
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* combination of 8 features
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* predictive linear regression model
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RMSE| 0.81
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R-Squared| 0.60
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MSE| 0.65
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MAE| 0.64
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r| 0.7978
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%section#darkness_rater_agreement
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:markdown
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Rater Agreement
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===============
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Intraclass Correlation Coefficient (Two-Way Model, Consistency):
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**0.498**
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%footer
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%section#further_resultes_conclusion
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:markdown
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Further Results & Conclusions
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=================================
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Comparison
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----------
|
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|
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When comparing <q>darkness</q> and <q>hardness</q>, the results
|
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indicate that the latter concept can be more efficiently described
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and modeled by specific sound attributes:
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* The consistency between ratings given by different raters is
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higher for <q>hardness</q> (see Intraclass Correlation
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Coefficients)
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* For the <q>hardness</q> dimension, a model can be based on a more
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compact set of features and at the same time leads to a better
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prediction rate
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Further application
|
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-------------------
|
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Although a considerable linear relation
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(<nobr>r = 0.65</nobr>, <nobr>p < 0.01</nobr>) is present between
|
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the two dimensions within the studied dataset, the concepts prove to
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be useful criteria for distinguishing music examples from different
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genres.
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E.g. a simple tree can be constructed for classification into broad
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genre categories (Pop, Techno, Metal, Gothic) with an accuracy of
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74%.
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%img(src="files/predictionTree_genreAgg2.png")
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%img(src="files/confusionMatrix_simpleTree_genreAgg2.png")
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%section#conclusion
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:markdown
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Conclusion
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==========
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<q>Hardness</q> and <q>darkness</q> constitute perceptually relevant
|
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dimensions for a high-level description of music. By decoding the
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sound characteristics associated with these concepts, they can be
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used for analyzing and indexing music collections and e.g. in a
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decision tree for automatic genre prediction.
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-#%section#ergebnisse1(style="height:96.35cm")
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%h1 4. Ergebnisse
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%figure.right(style="width:70%")
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%img(alt='Verwelkter Mohn' src='files/violin_genre_darkMean.svg')
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@ -253,65 +380,22 @@
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%div(style="clear:left")
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.clear
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%footer
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%section#literatur
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%section#references
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-#(style="width:44.5%;display:inline-block;float:right")
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%h1 6. Literatur
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%h1 References
|
||||
%ul.literatur
|
||||
%li
|
||||
%span.author Bismarck, G. v.
|
||||
%span.year 1974
|
||||
%span.title Sharpness as an attribute of the timbre of steady sounds
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||||
%nobr
|
||||
%span.book Acta Acustica united with Acustica 30.3
|
||||
%span.pages 159–172
|
||||
%li
|
||||
%span.author Bogdanov, D., Wack N., Gómez E., Gulati S., Herrera P., Mayor O., et al.
|
||||
%span.year 2013
|
||||
%span.title ESSENTIA: an Audio Analysis Library for Music Information Retrieval
|
||||
%nobr
|
||||
%span.herausgeber International Society for Music Information Retrieval Conference (ISMIR'13)
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||||
%span.pages 493-498
|
||||
%li
|
||||
%span.author Driedger, J. & Müller, M.
|
||||
%span.year 2014
|
||||
%span.title TSM Toolbox: MATLAB Implementations of Time-Scale Modification Algorithms
|
||||
%span.herausgeber Proc. of the International Conference on Digital Audio Effects
|
||||
%li
|
||||
%span.author Genesis
|
||||
%span.year 2009
|
||||
%span.title Loudness toolbox
|
||||
%span.author Czedik-Eysenberg, I., Knauf, D., & Reuter, C.
|
||||
%span.year 2017
|
||||
%span.title <q>Hardness</q> as a semantic audio descriptor for music using automatic feature extraction
|
||||
%span.herausgeber Gesellschaft für Informatik, Bonn
|
||||
%span.link
|
||||
%a(href="http://www.genesis-acoustics.com/en/index.php?page=32")
|
||||
http://www.genesis-acoustics.com/en/index.php?page=32
|
||||
%a(href="https://doi.org/10.18420/in2017_06") https://doi.org/10.18420/in2017_06
|
||||
%li
|
||||
%span.author Grey, J.M.
|
||||
%span.year 1975
|
||||
%span.title An Exploration of Musical Timbre
|
||||
%span.herausgeber Stanford University, CCRMA Report No.STAN-M-2
|
||||
%li
|
||||
%span.author Hevner, K.
|
||||
%span.year 1936
|
||||
%span.title Experimental studies of the elements of expression in music
|
||||
%nobr
|
||||
%span.herausgeber The American Journal of Psychology, 48(2)
|
||||
%span.pages 246-268
|
||||
%li
|
||||
%span.author Lartillot, O., & Toiviainen, P.
|
||||
%span.year 2007
|
||||
%span.title A Matlab toolbox for musical feature extraction from audio
|
||||
%nobr
|
||||
%span.herausgeber International Conference on Digital Audio Effects, Bordeaux
|
||||
%span.pages 237-244
|
||||
%li
|
||||
%span.author Laurier, C., Meyers, O., Serrà, J., Blech, M., Herrera, P., & Serra, X.
|
||||
%span.year 2010
|
||||
%span.title Indexing music by mood: Design and integration of an automatic content-based annotator
|
||||
%nobr
|
||||
%span.herausgeber Multimedia Tools and Applications, 48(1)
|
||||
%span.pages 161-184
|
||||
%span.link
|
||||
%a(href="http://dx.doi.org/10.1007/s11042-009-0360-2") http://dx.doi.org/10.1007/s11042-009-0360-2
|
||||
%li
|
||||
%span.author Li,T., Ogihara,M.
|
||||
%span.year 2003
|
||||
|
|
@ -333,11 +417,4 @@
|
|||
%nobr
|
||||
%span.herausgeber 40. DAGA
|
||||
%span.pages 56-57
|
||||
%li
|
||||
%span.author Wedin, L. & Goude, G.
|
||||
%span.year 1972
|
||||
%span.title Dimension analysis of the perception of instrumental timbre
|
||||
%nobr
|
||||
%span.herausgeber Scandinavian Journal of Psychology 13.1
|
||||
%span.pages 228–240
|
||||
.clear
|
||||
|
|
|
|||
41
style.scss
41
style.scss
|
|
@ -144,6 +144,7 @@ footer {
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|||
body {
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||||
margin: 0;
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background: url(files/marble_black.png); //url(brushed-metal.new.svg);
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||||
background: url(brushed-metal.pink.svg);
|
||||
color: #565655;
|
||||
font-family: "Cardo";
|
||||
}
|
||||
|
|
@ -182,7 +183,8 @@ section {
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|||
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||||
&::before {
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||||
z-index: -1;
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||||
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||||
background: linear-gradient( rgba(49, 206, 15, 0.8) 2.2rem, rgba(256, 256, 256, 0.8) 2.3rem );
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||||
content: "";
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||||
//border-radius: 2rem 2rem 0.5rem 0.5rem;
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position: absolute;
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|
@ -199,7 +201,7 @@ section {
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|||
//border-bottom: 0.3rem solid black
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//border-radius: 0.18rem 1.68rem 0 0
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||||
font-size: 1.8rem;
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@ -245,7 +247,8 @@ quellen {
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Loading…
Reference in New Issue