diff --git a/.gitignore b/.gitignore
index f0da397..dd62e6c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,4 +7,5 @@ fonts/*.map
style.css
*.swp
/.bundle
+/files/einhorn
/vendor/bundle
diff --git a/brushed-metal.dark.svg b/brushed-metal.dark.svg
index 964565f..3ca9556 100644
--- a/brushed-metal.dark.svg
+++ b/brushed-metal.dark.svg
@@ -24,11 +24,11 @@
inkscape:collect="always"
id="linearGradient5938">
@@ -37,11 +37,11 @@
id="linearGradient4468"
osb:paint="gradient">
diff --git a/files/LastFM.png b/files/LastFM.png
old mode 100755
new mode 100644
diff --git a/files/blunt_chromagram.png b/files/blunt_chromagram.png
old mode 100755
new mode 100644
diff --git a/files/blunt_dyndist.png b/files/blunt_dyndist.png
old mode 100755
new mode 100644
diff --git a/files/blunt_envelope.png b/files/blunt_envelope.png
old mode 100755
new mode 100644
diff --git a/files/confusionMatrix_simpleTree_genreAgg2.png b/files/confusionMatrix_simpleTree_genreAgg2.png
old mode 100755
new mode 100644
diff --git a/files/decap_chromagram.png b/files/decap_chromagram.png
old mode 100755
new mode 100644
diff --git a/files/decap_dyndist.png b/files/decap_dyndist.png
old mode 100755
new mode 100644
diff --git a/files/decap_envelope.png b/files/decap_envelope.png
old mode 100755
new mode 100644
diff --git a/files/diagramm_vorgang_english.png b/files/diagramm_vorgang_english.png
old mode 100755
new mode 100644
diff --git a/files/hammer-306313_960_720.png b/files/hammer-306313_960_720.png
new file mode 100644
index 0000000..698751e
Binary files /dev/null and b/files/hammer-306313_960_720.png differ
diff --git a/files/predictionTree_genreAgg2.png b/files/predictionTree_genreAgg2.png
old mode 100755
new mode 100644
diff --git a/files/predictionTree_genreAgg2.svg b/files/predictionTree_genreAgg2.svg
new file mode 100644
index 0000000..73f90a0
--- /dev/null
+++ b/files/predictionTree_genreAgg2.svg
@@ -0,0 +1,94 @@
+
+
+
diff --git a/files/scatter_darkness_model8.png b/files/scatter_darkness_model8.png
old mode 100755
new mode 100644
diff --git a/files/scatter_hard_dark_dashedline_2017-09-05.png b/files/scatter_hard_dark_dashedline_2017-09-05.png
old mode 100755
new mode 100644
diff --git a/files/scatter_hardness_model5.png b/files/scatter_hardness_model5.png
old mode 100755
new mode 100644
diff --git a/files/scatter_spectral_centroid_essentia_darkness.png b/files/scatter_spectral_centroid_essentia_darkness.png
old mode 100755
new mode 100644
diff --git a/files/sonagramm_blunt_log.png b/files/sonagramm_blunt_log.png
old mode 100755
new mode 100644
diff --git a/files/sonagramm_decap_log.png b/files/sonagramm_decap_log.png
old mode 100755
new mode 100644
diff --git a/files/thor-hammer3.png b/files/thor-hammer3.png
new file mode 100644
index 0000000..5e69a26
Binary files /dev/null and b/files/thor-hammer3.png differ
diff --git a/files/univie_logo.png b/files/univie_logo.png
old mode 100755
new mode 100644
diff --git a/files/violin_keyEdma_darkMean_blaugelb.png b/files/violin_keyEdma_darkMean_blaugelb.png
old mode 100755
new mode 100644
diff --git a/index.html.haml b/index.html.haml
index a5e96be..df584f2 100644
--- a/index.html.haml
+++ b/index.html.haml
@@ -19,10 +19,10 @@
%head
-#%meta(charset="utf-8")
%title Decoding the sound of 'hardness' and 'darkness' as perceptual dimensions of music
- -#%link(rel="stylesheet" href="fonts/Roboto.css")
- -#%link(rel="stylesheet" href="fonts/RobotoSlab.css")
+ %link(rel="stylesheet" href="fonts/Roboto.css")
+ %link(rel="stylesheet" href="fonts/RobotoSlab.css")
-#%link(rel="stylesheet" href="fonts/PT_Mono.css")
- -#%link(rel="stylesheet" href="fonts/PT_Sans.css")
+ %link(rel="stylesheet" href="fonts/PT_Sans.css")
-#%link(rel="stylesheet" href="fonts/Vollkorn.css")
-#%link(rel="stylesheet" href="fonts/Asset.css")
-#%link(rel="stylesheet" href="fonts/WithinDestruction.css")
@@ -39,8 +39,8 @@
%body
%header(style="")
%figure.logos(style="margin-top:0.3cm")<>
- %img#tagungs-logo(style="float:right" src="files/icmpc15_logo.png")
- %img#uni-logo(src="files/Uni_Logo_2016_ausschnitt.gif")
+ %img#uni-logo(src="files/univie_logo.png")
+ %img#tagungs-logo(style="float:right;height:i3.5em" src="files/icmpc15_logo.png")
-#%div(style="font-size:0.8em;margin-top:1.31cm")
44. Jahrestagung für Akustik
%br<>
@@ -82,56 +82,85 @@
Considering Bonferroni correction, 65 significant feature
correlations were found for the concept of hardness
.
- The characterizing attributes of hardness
include high
- tempo and sound density, less focus on clear melodic lines than
- noise-like sounds and especially the occurrence of strong percussive
+ The characterizing attributes of hardness
include **high
+ tempo** and **sound density**, less focus on clear melodic lines than
+ **noise-like** sounds and especially the occurrence of strong **percussive**
components.
%ol
%li
- percussive energy / rhythmic density
- %figure
- %img.fifty(src="files/sonagramm_blunt_log.png")
- %img.fifty(src="files/sonagramm_decap_log.png")
+ %p percussive energy / rhythmic density
+ %figure.pfifty
+ %figcaption Spectrogram James Blunt - You're Beautiful
+ %img(src="files/sonagramm_blunt_log.png")
+ %figure.pfifty
+ %figcaption Spectrogram Decapitated - The Fury
+ %img(src="files/sonagramm_decap_log.png")
+ .clear
%li
- dynamic distribution
- %figure
- %img.fifty(src="files/blunt_envelope.png")
- %img.fifty(src="files/decap_envelope.png")
- %figure
- %img.fifty(src="files/blunt_dyndist.png")
- %img.fifty(src="files/decap_dyndist.png")
+ %p dynamic distribution
+ %figure.pfifty
+ %figcaption Dynamic Envelope James Blunt - You're Beautiful
+ %img(src="files/blunt_envelope.png")
+ %figure.pfifty
+ %figcaption Dynamic Envelope Decapitated - The Fury
+ %img(src="files/decap_envelope.png")
+ -#%figure.pfifty
+ %figcaption Dynamic distribution James Blunt - You're Beautiful
+ %img(src="files/blunt_dyndist.png")
+ -#%figure.pfifty
+ %figcaption Dynamic distribution Decapitated - The Fury
+ %img(src="files/decap_dyndist.png")
+ .clear
%li
- melodic content / harmonic entropy
- %figure
- %img.fifty(src="files/blunt_chromagram.png")
- %img.fifty(src="files/decap_chromagram.png")
- :markdown
- Model
- -----
+ %p melodic content / harmonic entropy
+ %figure.pfifty
+ %figcaption Chromagramm James Blunt - You're Beautiful
+ %img(src="files/blunt_chromagram.png")
+ %figure.pfifty
+ %figcaption Chromagram Decapitated - The Fury
+ %img(src="files/decap_chromagram.png")
+ .clear
- Sequential feature selection
-
- * set of 5 features
- * predictive linear regression model
-
- RMSE | 0.64
- R-Squared | 0.80
- MSE | 0.40
- MAE | 0.49
- r | 0.900
- %figure
- %img(src="scatter_hardness_model5.png")
+ -#%h2(style="margin-top:1.5em") Model
+ %h2(style="margin-top:40px") Model
+ %figure.fifty.left(style="width:67%;text-align:center")
+ %img(src="files/scatter_hardness_model5.png")
+ %div(style="display:inline-block")
+ :markdown
+ RMSE | R2 | MSE | MAE | r
+ 0.64 | 0.80 | 0.40 | 0.49 | 0.90
+ %p(style="text-align:center")<>
+ Sequential feature selection
+ %br<>
+ ↓
+ %br<>
+ set of 5 features
+ %br<>
+ ↓
+ %br<>
+ predictive linear regression model
+ -#
+ RMSE | 0.64
+ R2 | 0.80
+ MSE | 0.40
+ MAE | 0.49
+ r | 0.90
+ .clear
:markdown
Rater Agreement
---------------
- Intraclass Correlation Coefficient (Two-Way Model, Consistency): 0.653
+ Intraclass Correlation Coefficient (Two-Way Model, Consistency): 0.653
.clear
#column1_2
- -#%section#aims
+ %section#aims
%h1 Aims
%p
+ The semantic concepts of hardness
and darkness
in music are analyzed
+ in terms of their corresponding sound attributes. Based on listening test data,
+ predictive models for both dimensions are created and compared.
+ -#%p
Based on computationally obtainable signal features, the creation
of models for the perceptual concepts of hardness
and
darkness
in music is aimed for. Furthermore it shall be
@@ -140,7 +169,7 @@
these dimensions.
%section#method
%h1 Method
- %figure.right(style="width:50%")
+ %figure.right(style="width:12%;height:2em;margin: 0.5em 0.5em 0.5em 1.5em")
%img(src="files/LastFM.png")
%p
Based on last.fm listener statistics, 150 pieces of music were selected
@@ -151,7 +180,8 @@
These ratings served as a ground truth for examining the two
concepts using a machine learning approach:
- %figure.right(style="width:50%")
+ %figure.right
+ //(style="width:50%")
%img(src="files/diagramm_vorgang_english.png")
%p
Taking into account 230 features describing spectral distribution,
@@ -159,45 +189,25 @@
investigated and combined into models.
Predictors were trained using five-fold cross-validation.
.clear
- %h2 Data
+
+ -#.blockarrow(style="display:block;width:100%;font-size:6em;margin:0") 🠳
+ %section#data(style="margin-top:2em")
+ %h1 Data
%figure
%img(src="files/scatter_hard_dark_dashedline_2017-09-05.png")
+ .blockarrow(style="top:-3.8rem;left:0;right:0") 🡇
+ .blockarrow(style="bottom:9rem;left:-3rem") 🡄
+ .blockarrow(style="bottom:9rem;right:-3rem") 🡆
.clear
- %section#further_resultes_conclusion
- %h1 Further Results & Conclusions
- %figure.fifty
- %img.right(src="files/predictionTree_genreAgg2.png")
- %img.right(src="files/confusionMatrix_simpleTree_genreAgg2.png")
- :markdown
- Comparison
- ----------
-
- When comparing darkness
and hardness
, the results
- indicate that the latter concept can be more efficiently described
- and modeled by specific sound attributes:
-
- * The consistency between ratings given by different raters is
- higher for hardness
(see Intraclass Correlation
- Coefficients)
- * For the hardness
dimension, a model can be based on a more
- compact set of features and at the same time leads to a better
- prediction rate
-
- Further application
- -------------------
-
- Although a considerable linear relation
- (r = 0.65, p < 0.01) is present between
- the two dimensions within the studied dataset, the concepts prove to
- be useful criteria for distinguishing music examples from different
- genres.
-
- E.g. a simple tree can be constructed for classification into broad
- genre categories (Pop, Techno, Metal, Gothic) with an accuracy of
- 74%.
+ %div(style="margin-top:1em;margin-bottom:-1em")
+ %div(style="width:40%;display:inline-block;float:left;text-align:center")
+ -#%img(src="files/hammer-306313_960_720.png" style="height:5em")
+ %img(src="files/thor-hammer3.png" style="height:5em")
+ .blockarrow(style="display:block;width:100%;font-size:7.5rem;margin:0;margin-top:-1.3rem") 🡇
+ %div(style="width:40%;display:inline-block;float:right;text-align:center")
+ %img(src="files/Candle.png" style="height:5em")
.clear
-
#column1_3
%section#darkness
%h1 Darkness
@@ -225,92 +235,144 @@
correlations were found for the darkness
ratings.
While a suspected negative correlation with **timbral
- brightness
** cannot be confirmed, darkness
appears to
+ brightness
** can **not** be confirmed, darkness
appears to
be associated with a high **spectral complexity** and harmonic
traits like **major or minor mode**.
- %figure.fifty
+ %figure.fifty.left
%img(src="files/scatter_spectral_centroid_essentia_darkness.png")
- :markdown
- Correlations between darkness rating and measures for brightness:
+ %div(style="height:1em")
+ %p No evidence for negative 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
+ %div(style="text-align:center")
+ %div(style="display:inline-block")
+ :markdown
+ Feature | r | p
+ -----------------------|-------|----------
+ Spectral centroid | 0.334 | <0.01
+ High frequency content | 0.153 | 0.063
+ %figure.fifty(style="margin-top:0.4em")
%img(src="files/violin_keyEdma_darkMean_blaugelb.png")
%p
Musical excerpts in minor mode were significantly rated as
harder
than those in major mode. (p < 0.01
according to t-test)
%h2 Model
- %figure.fifty
+ %figure.fifty.right(style="width:67%;text-align:center;margin-bottom:3px")
%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
+ %div(style="display:inline-block")
+ :markdown
+ RMSE | R2 | MSE | MAE | r
+ 0.81 | 0.60 | 0.65 | 0.64 | 0.798
+ %p(style="text-align:center")<>
+ Sequential feature selection
+ %br<>
+ ↓
+ %br<>
+ set of 8 features
+ %br<>
+ ↓
+ %br<>
+ predictive linear regression model
+ -#
+ RMSE | 0.81
+ R2 | 0.60
+ MSE | 0.65
+ MAE | 0.64
+ r | 0.798
+ .clear
:markdown
Rater Agreement
---------------
- Intraclass Correlation Coefficient (Two-Way Model, Consistency):
- **0.498**
+ Intraclass Correlation Coefficient (Two-Way Model, Consistency):
+ 0.498
.clear
- %footer
- %section#conclusion
- :markdown
- Conclusion
- ==========
+ %footer(style="padding-top:0.2em")
+ %section#further_resultes_conclusion(style="padding-bottom:0.20em")
+ %h1 Further Results & Conclusions
+ %div
+ #column2_1
+ :markdown
+ Comparison
+ ----------
- Hardness
and darkness
constitute perceptually relevant
- dimensions for a high-level description of music. By decoding the
- 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.
+ When comparing darkness
and hardness
, the results
+ indicate that the latter concept can be more efficiently described
+ and modeled by specific sound attributes:
- %section#references
- -#(style="width:44.5%;display:inline-block;float:right")
- %h1 References
- %ul.literatur
- %li
- %span.author Czedik-Eysenberg, I., Knauf, D., & Reuter, C.
- %span.year 2017
- %span.title Hardness
as a semantic audio descriptor for music using automatic feature extraction
- %span.herausgeber Gesellschaft für Informatik, Bonn
- %span.link= link '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 Li,T., Ogihara,M.
- %span.year 2003
- %span.title Detecting emotion in music
- %nobr
- %span.herausgeber 4th ISMIR Washington & Baltimore
- %span.pages 239-240
- %li
- %span.author Huron, D.
- %span.year 2008
- %span.title A comparison of average pitch height and interval size in major-and minor-key themes
- %nobr
- %span.herausgeber Empirical Musicology Review, 3
- %span.pages 59-63
- %li
- %span.author Siddiq,S. et al.
- %span.year 2014
- %span.title Kein Raum für Klangfarben - Timbre Spaces im Vergleich
- %nobr
- %span.herausgeber 40. DAGA
- %span.pages 56-57
- .clear
+ * The consistency between ratings given by different raters is
+ higher for hardness
(see Intraclass Correlation
+ Coefficients)
+ * For the hardness
dimension, a model can be based on a more
+ compact set of features and at the same time leads to a better
+ prediction rate
+
+ #column2_2
+ :markdown
+ Further application
+ -------------------
+
+ %figure.fifty(style="width:37%")
+ %img(src="files/confusionMatrix_simpleTree_genreAgg2.png")
+ :markdown
+ Although a considerable linear relation
+ (r = 0.65, p < 0.01) is present between
+ the two dimensions within the studied dataset, the concepts prove to
+ be useful criteria for distinguishing music examples from different
+ genres.
+ %figure.quarterly(style="clear:initial;width:28%")
+ %img(src="files/predictionTree_genreAgg2.svg")
+ %p
+ E.g. a simple tree can be constructed for classification into broad
+ genre categories (Pop, Techno, Metal, Gothic) with an accuracy of
+ 74 %.
+
+ #column2_3
+ :markdown
+ Conclusion
+ ----------
+
+ Hardness
and darkness
constitute perceptually relevant
+ dimensions for a high-level description of music. By decoding the
+ 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#references
+ -#(style="width:44.5%;display:inline-block;float:right")
+ %h1 References
+ %ul.literatur
+ %li
+ %span.author Czedik-Eysenberg, I., Knauf, D., & Reuter, C.
+ %span.year 2017
+ %span.title Hardness
as a semantic audio descriptor for music using automatic feature extraction
+ %span.herausgeber Gesellschaft für Informatik, Bonn
+ %span.link= link '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 Li,T., Ogihara,M.
+ %span.year 2003
+ %span.title Detecting emotion in music
+ %nobr
+ %span.herausgeber 4th ISMIR Washington & Baltimore
+ %span.pages 239-240
+ %li
+ %span.author Huron, D.
+ %span.year 2008
+ %span.title A comparison of average pitch height and interval size in major-and minor-key themes
+ %nobr
+ %span.herausgeber Empirical Musicology Review, 3
+ %span.pages 59-63
+ %li
+ %span.author Siddiq,S. et al.
+ %span.year 2014
+ %span.title Kein Raum für Klangfarben - Timbre Spaces im Vergleich
+ %nobr
+ %span.herausgeber 40. DAGA
+ %span.pages 56-57
+ .clear
diff --git a/style.scss b/style.scss
index 8f70d9d..3a848ea 100644
--- a/style.scss
+++ b/style.scss
@@ -63,7 +63,6 @@ header {
}
h1 {
- //font-family: "Italianno";
font-weight: normal;
margin: 0 {
//bottom: 0.5rem;
@@ -101,7 +100,7 @@ header {
padding: 0 1rem 0 1rem;
position: relative;
- color: #bbb;
+ color: #ddd;
text-shadow: 0 0 5px black, 0 0 10px black, 0 0 15px black;
//text-stroke: 1px black;
//-webkit-text-stroke: 1px black;
@@ -132,11 +131,14 @@ header, main, footer {
}
footer {
- padding-top: 0.5em;
+ margin-left: auto;
+ margin-right: auto;
+ padding: 0 0.45em 0 0.45em;
+ //padding-top: 0.5em;
section {
- padding: 2.25rem 0.5rem 0.25rem 0.5rem;
+ //padding: 2.25rem 0.5rem 0.25rem 0.5rem;
h1:first-child {
- margin: (-2.25rem) -0.5rem 0.25rem -0.5rem;
+ //margin: (-2.25rem) -0.5rem 0.25rem -0.5rem;
}
}
}
@@ -144,6 +146,7 @@ footer {
body {
margin: 0;
background: url(brushed-metal.dark.svg), url(files/marble_black.png), #252220;
+ //background: #252220;
color: #565655;
font-family: "Cardo";
}
@@ -166,7 +169,7 @@ section {
font-size: 0.95em;
//text-align: justify;
- &:first-child + * {
+ & + * {
margin-top: 1em;
}
@@ -187,7 +190,10 @@ section {
&::before {
z-index: -1;
//background: linear-gradient( rgba(44, 58, 41, 0.8) 2.2rem, rgba(256, 256, 256, 0.8) 2.3rem );
- background: linear-gradient( rgba(49, 206, 15, 0.8) 2.2rem, rgba(256, 256, 256, 0.8) 2.3rem );
+ //background: linear-gradient( rgba(101, 42, 7, 0.8) 2.2rem, rgba(256, 256, 256, 0.85) 2.3rem );
+ //background: linear-gradient( rgba(0, 99, 166, 0.8) 2.2rem, rgba(256, 256, 256, 0.85) 2.3rem );
+ //background: linear-gradient( rgba(187, 187, 187, 0.8) 2.2rem, rgba(256, 256, 256, 0.85) 2.3rem );
+ background: linear-gradient( rgba(78, 83, 159, 0.8) 2.2rem, rgba(256, 256, 256, 0.85) 2.3rem );
content: "";
//border-radius: 2rem 2rem 0.5rem 0.5rem;
position: absolute;
@@ -195,7 +201,7 @@ section {
right: 0;
bottom: 0;
left: 0;
- box-shadow: 0 0 1rem #555;
+ box-shadow: 0 0 1rem black;
-webkit-print-color-adjust: exact;
-webkit-filter: opacity(1);
}
@@ -207,18 +213,31 @@ section {
//border-bottom: 0.3rem solid black
//border-radius: 0.18rem 1.68rem 0 0
font-size: 1.8rem;
- color: orange;
+ //color: #0063a6;
+ color: #ddd;
line-height: normal;
text-align: center;
- font-family: "Italianno";
- font-weight: normal;
//border-radius: 0.5rem 2rem 0 0
//padding: 0.1em 0.5rem;
- margin: (-2.5rem) -1rem 0.5rem -1rem;
+ margin: (-2.25rem) -1rem 0.75rem -1rem;
//background-color: rgba(128,128,256,0.8)
}
+ h1, h2 {
+ font-family: "PT Slab";
+ font-weight: normal;
+ margin: 0;
+ }
+ h2 {
+ line-height: normal;
+ }
+}
+
+sup {
+ font-size: 0.65em;
+ vertical-align: top;
+ line-height: 1em;
}
quellen {
@@ -254,11 +273,11 @@ quellen {
}
#column1_1, #column1_2, #column1_3,
-#column2_1, #column2_2 {
+#column2_1, #column2_2, #column2_3 {
display: inline-block;
box-sizing: border-box;
margin: 0;
- padding: 0 1em 0.5em 1em;
+ padding: 0 0.3em 0.5em 0.3em;
vertical-align: top;
position: relative;
@@ -275,7 +294,7 @@ quellen {
}
#column1_1 {
- width: 33%;
+ width: 29%;
//padding-right: 0.5em;
}
@@ -283,21 +302,24 @@ quellen {
//float: right;
//padding-left: 0.7em;
//margin-left: -0.5em;
- width: 33%;
+ width: 41%;
}
#column1_3 {
//float: right;
//padding-left: 0.7em;
//margin-left: -0.5em;
- width: 33%;
+ width: 29%;
}
#column2_1 {
- width: 49%;
+ width: 30%;
}
#column2_2 {
width: 49%;
}
+#column2_3 {
+ width: 20%;
+}
.logos {
width: 19rem;
@@ -350,6 +372,9 @@ h1 {
font-family: "Cardo";
//font-weight: normal;
}
+h2 {
+ font-family: "Cardo";
+}
em {
color: #500;
@@ -375,15 +400,21 @@ feature {
}
main {
- position: absolute;
- top: 10.5cm;
- right: 0;
- left: 0;
+ text-align: center;
+ &>*{
+ text-align: initial;
+ }
+ position: relative;
+ //top: 10.5cm;
+ //right: 0;
+ //left: 0;
/*img {
margin: -0.5em;
}*/
+}
+main, footer {
figure {
margin: 0;
&.left {
@@ -411,6 +442,12 @@ main {
clear: left;
}
}
+ &.pfifty {
+ width: 47%;
+ display: inline-block;
+ vertical-align: bottom;
+ text-align: center;
+ }
&.quarterly {
float: right;
@@ -462,10 +499,11 @@ footer #literatur {
table {
border-collapse: collapse;
border-spacing: 0;
+ font-size: 0.8em;
th, td {
border: 1px solid #aaa;
- padding: 0.1em;
+ padding: 0.1em 0.5em;
}
}
@@ -549,9 +587,10 @@ ul.literatur {
}
figcaption {
- font-size: 0.8em;
+ font-size: 0.45em;
font-style: italic;
- margin-bottom: 1em;
+ //margin-bottom: 1em;
+ padding: 0;
}
jbr {
@@ -559,3 +598,19 @@ jbr {
width: 100%;
height: 0;
}
+
+.blockarrow {
+ line-height: 1em;
+ position: absolute;
+ text-align: center;
+ font-size: 4.5rem;
+ //color: #652a07;
+ color: #bbb;
+ text-shadow: 0 0 0.1em black, 0 0 0.1em black, 0 0 0.1em black;
+}
+
+.col2center {
+ td:first-child + td {
+ text-align: center;
+ }
+}