aufteilung, schlechte bilder.

brushed-metal.dark.svg

@@ -0,0 +1,144 @@
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index.html.haml

@@ -1,5 +1,4 @@
 - require "base64"
-~ "\xEF\xBB\xBF"
 - def quellen opts
   - etc = opts.key? :etc
   - if etc
@@ -14,6 +13,7 @@
   - mime_type = IO.popen(["file", "--brief", "--mime-type", file], in: :close, err: :close) { |io| io.read.chomp }
   - content = Base64.urlsafe_encode64 File.read( file)
   - "data:#{mime_type};base64,#{content}"
+~ "\xEF\xBB\xBF"
 !!! 5
 %html(lang='en')
   %head
@@ -39,8 +39,8 @@
   %body
     %header(style="")
       %figure.logos(style="margin-top:0.3cm")<>
-        %img#tagungs-logo(style="float:right" src="files/icmpc15_logo.jpg")
+        %img#tagungs-logo(style="float:right" src="files/icmpc15_logo.png")
-        %img#uni-logo(src="files/univie_logo.png")
+        %img#uni-logo(src="files/Uni_Logo_2016_ausschnitt.gif")
         -#%div(style="font-size:0.8em;margin-top:1.31cm")
           44. Jahrestagung für Akustik
           %br<>
@@ -65,10 +65,19 @@
 
     %main
       #column1_1
-        %section#heavy_features
+        %section#hardness
+          %h1 Hardness
+          %p
+            <q>Hardness</q> is often considered a distinctive feature of (heavy)
+            metal music, as well as in genres like hardcore techno or <q>Neue
+            Deutsche Härte</q>.
+            In a previous investigation the concept of <q>hardness</q> in music
+            was examined in terms of its acoustic correlates and suitability as
+            a descriptor for music #{quellen 'Czedik-Eysenberg et al.' => 2017}.
+
           :markdown
             Sound Features
-            ==============
+            --------------
 
             Considering Bonferroni correction, 65 significant feature
             correlations were found for the concept of <q>hardness</q>.
@@ -81,24 +90,25 @@
             %li
               percussive energy / rhythmic density
               %figure
-                %img(style="width:50%" src="files/sonagramm_blunt_log.png")
+                %img.fifty(src="files/sonagramm_blunt_log.png")
-                %img(style="width:50%" src="files/sonagramm_decap_log.png")
+                %img.fifty(src="files/sonagramm_decap_log.png")
             %li
               dynamic distribution
               %figure
-                %img(style="width:50%" src="files/blunt_envelope.png")
+                %img.fifty(src="files/blunt_envelope.png")
-                %img(style="width:50%" src="files/decap_envelope.png")
+                %img.fifty(src="files/decap_envelope.png")
               %figure
-                %img(style="width:50%" src="files/blunt_dyndist.png")
+                %img.fifty(src="files/blunt_dyndist.png")
-                %img(style="width:50%" src="files/decap_dyndist.png")
+                %img.fifty(src="files/decap_dyndist.png")
             %li
               melodic content / harmonic entropy
               %figure
-                %img(style="width:50%" src="files/blunt_chromagram.png")
+                %img.fifty(src="files/blunt_chromagram.png")
-                %img(style="width:50%" src="files/decap_chromagram.png")
+                %img.fifty(src="files/decap_chromagram.png")
-        %section#heavy_model
-          %h1 Model
           :markdown
+            Model
+            -----
+
             Sequential feature selection  
 
             * set of 5 features 
@@ -111,12 +121,12 @@
             r         | 0.900
           %figure
             %img(src="scatter_hardness_model5.png")
-        %section#heavy_rater_agreement
           :markdown
             Rater Agreement
-            ===============
+            ---------------
 
             Intraclass Correlation Coefficient  (Two-Way Model, Consistency): <b>0.653</b>
+          .clear
 
       #column1_2
         -#%section#aims
@@ -132,110 +142,33 @@
           %h1 Method
           %figure.right(style="width:50%")
             %img(src="files/LastFM.png")
-          :markdown
+          %p
             Based on last.fm listener statistics, 150 pieces of music were selected
             from 10 different subgenres of metal, techno, gothic and pop music.
-
+          %p
             In an online listening test, 40 participants were asked to rate the
             refrain of each example in terms of <q>hardness</q> and <q>darkness</q>.
             These ratings served as a ground truth for examining the two
             concepts using a machine learning approach:
 
+          %figure.right(style="width:50%")
+            %img(src="files/diagramm_vorgang_english.png")
+          %p
             Taking into account 230 features describing spectral distribution,
             temporal and dynamic properties, relevant dimensions were
             investigated and combined into models.
             Predictors were trained using five-fold cross-validation.
-          %figure.right(style="width:50%")
+          .clear
-            %img(src="files/einhorn/diagramm_vorgang_english.png")
+          %h2 Data
-        %section#data
+          %figure
-          %h1 Data
-          %figure.right(style="width:50%")
             %img(src="files/scatter_hard_dark_dashedline_2017-09-05.png")
-        %section#hardness
+          .clear
-          %h1 Hardness
-          %p
-            <q>Hardness</q> is often considered a distinctive feature of (heavy)
-            metal music, as well as in genres like hardcore techno or <q>Neue
-            Deutsche Härte</q>.
-            In a previous investigation the concept of <q>hardness</q> in music
-            was examined in terms of its acoustic correlates and suitability as
-            a descriptor for music #{quellen 'Czedik-Eysenberg et al.' => 2017}.
-
-      #column1_3
-        %section#darkness
-          %h1 Darkness
-          %p
-            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.
-        %section#darkness_features
-          :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**.
-          %figure
-            %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 | &lt;0.01
-            High frequency content | 0.1526 | 0.0631
-          %figure
-            %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 &lt; 0.01</nobr>
-            according to t-test)
-        %section#darkness_model
-          %h1 Model
-          %figure
-            %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
-        %section#darkness_rater_agreement
-          :markdown
-            Rater Agreement
-            ===============
-
-            Intraclass Correlation Coefficient (Two-Way Model, Consistency):
-            **0.498**
-
-      %footer
         %section#further_resultes_conclusion
+          %h1 Further Results &amp; Conclusions
+          %figure.fifty
+            %img.right(src="files/predictionTree_genreAgg2.png")
+            %img.right(src="files/confusionMatrix_simpleTree_genreAgg2.png")
           :markdown
-            Further Results &amp; Conclusions
-            =================================
-
             Comparison
             ----------
 
@@ -262,8 +195,77 @@
             E.g. a simple tree can be constructed for classification into broad
             genre categories (Pop, Techno, Metal, Gothic) with an accuracy of
             74%.
-          %img(src="files/predictionTree_genreAgg2.png")
+          .clear
-          %img(src="files/confusionMatrix_simpleTree_genreAgg2.png")
+
+
+      #column1_3
+        %section#darkness
+          %h1 Darkness
+          %p
+            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**.
+          %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 | &lt;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 &lt; 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|&lt; 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|&lt; 0,0001
-                RMS Gammatone 4|- 0,3427|&lt; 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 &lt; 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| &lt; 0,0001
-            HPCP Entropy (mean)| 0,5355| &lt; 0,0001
-            Dynamic Complexity| - 0,4855| &lt; 0,0001
-            Onset Rate| - 0,4837| &lt; 0,0001
-            Pitch Salience| 0,4835| &lt; 0,0001
-            MFCC 3 (mean)| 0,4657| &lt; 0,0001
-            Spectral Centroid (mean)| 0,3340| &lt; 0,0001
-            RMS Energy Gammatone 4| - 0,3427| &lt; 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
+              %span.link= link 'https://doi.org/10.18420/in2017_06'
-                %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

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.dark.svg), url(files/marble_black.png), #252220;
-	background: url(brushed-metal.pink.svg);
 	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