aufteilung, schlechte bilder.
Denis Knauf
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328
index.html.haml
328
index.html.haml
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@ -1,5 +1,4 @@
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- require "base64"
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~ "\xEF\xBB\xBF"
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- def quellen opts
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- etc = opts.key? :etc
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- if etc
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@ -14,6 +13,7 @@
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- mime_type = IO.popen(["file", "--brief", "--mime-type", file], in: :close, err: :close) { |io| io.read.chomp }
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- content = Base64.urlsafe_encode64 File.read( file)
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- "data:#{mime_type};base64,#{content}"
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~ "\xEF\xBB\xBF"
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!!! 5
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%html(lang='en')
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%head
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@ -39,8 +39,8 @@
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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#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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%img#tagungs-logo(style="float:right" src="files/icmpc15_logo.png")
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%img#uni-logo(src="files/Uni_Logo_2016_ausschnitt.gif")
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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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@ -65,10 +65,19 @@
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%main
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#column1_1
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%section#heavy_features
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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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:markdown
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Sound Features
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==============
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--------------
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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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@ -81,24 +90,25 @@
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%li
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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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%img.fifty(src="files/sonagramm_blunt_log.png")
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%img.fifty(src="files/sonagramm_decap_log.png")
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%li
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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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%img.fifty(src="files/blunt_envelope.png")
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%img.fifty(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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%img.fifty(src="files/blunt_dyndist.png")
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%img.fifty(src="files/decap_dyndist.png")
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%li
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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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%img.fifty(src="files/blunt_chromagram.png")
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%img.fifty(src="files/decap_chromagram.png")
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:markdown
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Model
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-----
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Sequential feature selection
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* set of 5 features
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@ -111,12 +121,12 @@
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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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---------------
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Intraclass Correlation Coefficient (Two-Way Model, Consistency): <b>0.653</b>
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.clear
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#column1_2
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-#%section#aims
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@ -132,110 +142,33 @@
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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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%p
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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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%p
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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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%figure.right(style="width:50%")
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%img(src="files/diagramm_vorgang_english.png")
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%p
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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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.clear
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%h2 Data
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%figure
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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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|
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%footer
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.clear
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%section#further_resultes_conclusion
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%h1 Further Results & Conclusions
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%figure.fifty
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%img.right(src="files/predictionTree_genreAgg2.png")
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%img.right(src="files/confusionMatrix_simpleTree_genreAgg2.png")
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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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@ -262,8 +195,77 @@
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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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.clear
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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
|
||||
metaphorical sense, especially in genres like gothic or doom metal.
|
||||
According to musical adjective classifications <q>dark</q> is part
|
||||
of the same cluster as <q>gloomy</q>, <q>sad</q> or
|
||||
<q>depressing</q> #{quellen Hevner: 1936}, which was later adopted in
|
||||
computational musical affect detection
|
||||
#{quellen 'Li & Oghihara' => 2003}.
|
||||
This would suggest the
|
||||
relevance of sound attributes that correspond with the expression
|
||||
of sadness, e.g. lower pitch, small pitch movement and <q>dark</q>
|
||||
timbre #{quellen Huron: 2008}. In timbre research <q>brightness</q>
|
||||
is often considered one of the central perceptual axes
|
||||
#{quellen Grey: 1975, 'Siddiq et al.' => 2014}, which raises the
|
||||
question if <q>darkness</q> in music is also reflected as the
|
||||
inverse of this timbral <q>brightness</q> concept.
|
||||
:markdown
|
||||
Sound Features
|
||||
--------------
|
||||
|
||||
Considering Bonferroni correction, 35 significant feature
|
||||
correlations were found for the <q>darkness</q> ratings.
|
||||
|
||||
While a suspected negative correlation with **timbral
|
||||
<q>brightness</q>** cannot be confirmed, <q>darkness</q> appears to
|
||||
be associated with a high **spectral complexity** and harmonic
|
||||
traits like **major or minor mode**.
|
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%figure.fifty
|
||||
%img(src="files/scatter_spectral_centroid_essentia_darkness.png")
|
||||
:markdown
|
||||
Correlations between darkness rating and measures for brightness:
|
||||
|
||||
Feature | r | p
|
||||
-----------------------|--------|----------
|
||||
Spectral centroid | 0.3340 | <0.01
|
||||
High frequency content | 0.1526 | 0.0631
|
||||
%figure.fifty
|
||||
%img(src="files/violin_keyEdma_darkMean_blaugelb.png")
|
||||
%p
|
||||
Musical excerpts in minor mode were significantly rated as
|
||||
<q>harder</q> than those in major mode. (<nobr>p < 0.01</nobr>
|
||||
according to t-test)
|
||||
%h2 Model
|
||||
%figure.fifty
|
||||
%img(src="files/scatter_darkness_model8.png")
|
||||
:markdown
|
||||
Sequential feature selection:
|
||||
|
||||
* combination of 8 features
|
||||
* predictive linear regression model
|
||||
|
||||
RMSE| 0.81
|
||||
R-Squared| 0.60
|
||||
MSE| 0.65
|
||||
MAE| 0.64
|
||||
r| 0.7978
|
||||
:markdown
|
||||
Rater Agreement
|
||||
---------------
|
||||
|
||||
Intraclass Correlation Coefficient (Two-Way Model, Consistency):
|
||||
**0.498**
|
||||
.clear
|
||||
|
||||
%footer
|
||||
%section#conclusion
|
||||
:markdown
|
||||
Conclusion
|
||||
|
|
@ -274,111 +276,6 @@
|
|||
sound characteristics associated with these concepts, they can be
|
||||
used for analyzing and indexing music collections and e.g. in a
|
||||
decision tree for automatic genre prediction.
|
||||
|
||||
-#%section#ergebnisse1(style="height:96.35cm")
|
||||
%h1 4. Ergebnisse
|
||||
%figure.right(style="width:70%")
|
||||
%img(alt='Verwelkter Mohn' src='files/violin_genre_darkMean.svg')
|
||||
%p
|
||||
Es zeigt sich ein Bezug zwischen dem Genre und der
|
||||
durchschnittlichen Düsterkeitsbewertung der jeweiligen Stimuli.
|
||||
%figure.right(style="width:35%")
|
||||
%img(alt='Ernstes Indigo' src='files/scatter_spectral_centroid_essentia_darkness.svg')
|
||||
%p
|
||||
Eine Antiproportionalität zu klangfarblicher <q>Helligkeit</q> lässt
|
||||
sich (mit der vorliegenden Messmethode) nicht nachweisen. Es liegt
|
||||
im Gegenteil sogar eine leicht positive Korrelation vor –
|
||||
womöglich u.a. bedingt durch erhöhte dissonante Klanganteile im
|
||||
Hochfrequenzbereich (z.B. Schlagzeugvorkommen). Werden die
|
||||
perkussiven Signalanteile zuvor ausgefiltert, verringert sich
|
||||
dieser Effekt bereits deutlich.
|
||||
%figure.nobrtd(style="width:24em")
|
||||
:markdown
|
||||
Merkmal|r|p
|
||||
---|---|---
|
||||
Spectral Centroid|0,3340|< 0,0001
|
||||
Hochfrequenzanteil (> 1500 Hz)|0,1526|0,0631
|
||||
Spectral Centroid (harmonischer Teil)|0,2094|0,0101
|
||||
Hochfrequenzanteil (harmonischer Teil)|0,1270|0,1215
|
||||
{:.merkmale}
|
||||
%figcaption
|
||||
Korrelation der durchschnittlichen Düsterkeits<wbr/>bewertung mit Maßen
|
||||
für klangfarbliche Helligkeit.
|
||||
|
||||
.clear
|
||||
%figure.left(style="width:41.1%")
|
||||
%img(alt='Trauriges Purpur' src='files/violin_keyEdma_darkMean_blaugelb.svg')
|
||||
|
||||
%figure
|
||||
%figure.right(style="width:12em")
|
||||
%img(alt="lilien grau" src="files/meanspectra_10khz_600dpi.png")
|
||||
%figure.right
|
||||
:markdown
|
||||
Merkmal|r|p
|
||||
---|---|---
|
||||
RMS Gammatone 1|- 0,3989|< 0,0001
|
||||
RMS Gammatone 4|- 0,3427|< 0,0001
|
||||
RMS Gammatone 5|- 0,3126|0,0001
|
||||
{:.merkmale}
|
||||
%p(style="clear:right")
|
||||
Zwischen den 30 am düstersten bzw. am wenigsten düster bewerteten
|
||||
Klangbeispielen zeigen sich charakteristische Unterschiede in der spektralen
|
||||
Verteilung (insbesondere im Bereich der Gammatone-Filterbank-Bänder 1, 4 und 5).
|
||||
|
||||
%p(style="clear:right")
|
||||
Ein deutlicher Zusammenhang zeigt sich mit der Tonart der
|
||||
jeweiligen Ausschnitte: Moll-Beispiele wurden im Durchschnitt als
|
||||
düsterer bewertet als Stücke in Dur-Tonarten (<nobr>p < 0.0001</nobr> laut t-Test).
|
||||
%p(style="clear:right")
|
||||
Teilweise eher statische Tonchroma-Veränderungen im Fall der als
|
||||
düster bewerteten Beispiele könnten die Theorie geringere
|
||||
Tonhöhenbewegungen in Zusammenhang mit einem Ausdruck von Trauer
|
||||
bestätigen (siehe z.B. Chromagramm <q><nobr>Sunn 0)))</nobr></q>).
|
||||
%figure.right(style="width:58.2%")
|
||||
%img(style="width:49%" alt='Schrumpeliges Gelb' src='files/chromagramm_sunn.svg')
|
||||
%img(style="width:49%" alt='Vergängliches Weiß' src='files/chromagramm_abba.svg')
|
||||
%p(style="clear:left;max-width: 50%")
|
||||
Der stärkste Zusammenhang lässt sich zur Spectral Complexity
|
||||
feststellen, welche die Komplexität des Signals in Bezug auf seine
|
||||
Frequenzkomponenten anhand der Anzahl spektraler Peaks im Bereich
|
||||
zwischen 100 Hz und 5 kHz beschreibt. Dies ist interessant mit den
|
||||
Ergebnissen von #{quellen 'Laurier et al.' => 2010} in Bezug zu setzen,
|
||||
welche beobachteten, dass <q>entspannte</q> (<q>relaxed</q>) Stücke eine
|
||||
niedrigere spektrale Komplexität aufweisen, <q>fröhliche</q> (<q>happy</q>)
|
||||
Stücke jedoch eine leicht höhere spektrale Komplexität als
|
||||
<q>nicht fröhliche</q>.
|
||||
%figure.left(style="width:59.83%;position:relative")
|
||||
%img(alt='Totes Grün' src='files/scatter_model8_mit_beschriftung_gross.svg')
|
||||
%img(alt="Farbiges Beispiel" style="width:5cm;opacity:0.7;position:absolute;top:0;left:3cm" src="files/bat.png")
|
||||
%p(style="clear:right")
|
||||
Nach sequentieller Merkmalsauswahl wurden 8 Signaldeskriptoren zur
|
||||
Bildung eines Modells zu Rate gezogen:
|
||||
:markdown
|
||||
Merkmal|r|p
|
||||
----|----|----
|
||||
Spectral Complexity (mean)| 0,6224| < 0,0001
|
||||
HPCP Entropy (mean)| 0,5355| < 0,0001
|
||||
Dynamic Complexity| - 0,4855| < 0,0001
|
||||
Onset Rate| - 0,4837| < 0,0001
|
||||
Pitch Salience| 0,4835| < 0,0001
|
||||
MFCC 3 (mean)| 0,4657| < 0,0001
|
||||
Spectral Centroid (mean)| 0,3340| < 0,0001
|
||||
RMS Energy Gammatone 4| - 0,3427| < 0,0001
|
||||
{:.merkmale}
|
||||
%p
|
||||
Anhand dieser wurde unter 5-facher Kreuzvalidierung ein lineares
|
||||
Regressionsmodell zur Abschätzung der Düsterkeitsbewertung erstellt.
|
||||
:markdown
|
||||
Merkmal|Wert
|
||||
----|----
|
||||
Root-mean-squared error (RMSE)|0,81<span class="hidden">00</span>
|
||||
Bestimmtheitsmaß (R<sup>2</sup>)|0,60<span class="hidden">00</span>
|
||||
Mean Squared Error (MSE)|0,65<span class="hidden">00</span>
|
||||
Mean Average Error (MAE)|0,64<span class="hidden">00</span>
|
||||
Korrelation (insgesamt)|0,7978
|
||||
{:.merkmale}
|
||||
%div(style="clear:left")
|
||||
.clear
|
||||
|
||||
%section#references
|
||||
-#(style="width:44.5%;display:inline-block;float:right")
|
||||
|
|
@ -389,8 +286,7 @@
|
|||
%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="https://doi.org/10.18420/in2017_06") https://doi.org/10.18420/in2017_06
|
||||
%span.link= link 'https://doi.org/10.18420/in2017_06'
|
||||
%li
|
||||
%span.author Grey, J.M.
|
||||
%span.year 1975
|
||||
|
|
|
|||
18
style.scss
18
style.scss
|
|
@ -143,8 +143,7 @@ footer {
|
|||
|
||||
body {
|
||||
margin: 0;
|
||||
background: url(files/marble_black.png); //url(brushed-metal.new.svg);
|
||||
background: url(brushed-metal.pink.svg);
|
||||
background: url(brushed-metal.dark.svg), url(files/marble_black.png), #252220;
|
||||
color: #565655;
|
||||
font-family: "Cardo";
|
||||
}
|
||||
|
|
@ -167,19 +166,23 @@ section {
|
|||
font-size: 0.95em;
|
||||
//text-align: justify;
|
||||
|
||||
&:first-child {
|
||||
&:first-child + * {
|
||||
margin-top: 1em;
|
||||
}
|
||||
|
||||
//&:first-child {
|
||||
&, &::before {
|
||||
border-top-right-radius: 2rem;
|
||||
border-top-left-radius: 2rem;
|
||||
//margin-top: 0;
|
||||
}
|
||||
}
|
||||
&:last-child {
|
||||
//}
|
||||
//&:last-child {
|
||||
&, &::before {
|
||||
border-bottom-right-radius: 0.5rem;
|
||||
border-bottom-left-radius: 0.5rem;
|
||||
}
|
||||
}
|
||||
//}
|
||||
|
||||
&::before {
|
||||
z-index: -1;
|
||||
|
|
@ -196,6 +199,9 @@ section {
|
|||
-webkit-print-color-adjust: exact;
|
||||
-webkit-filter: opacity(1);
|
||||
}
|
||||
&[header-background]::before {
|
||||
background: linear-gradient( rgba(205, 106, 81, 0.8) 2.2rem, rgba(256, 256, 256, 0.8) 2.3rem );
|
||||
}
|
||||
|
||||
h1:first-child {
|
||||
//border-bottom: 0.3rem solid black
|
||||
|
|
|
|||
Loading…
Reference in New Issue