final
This commit is contained in:
Denis Knauf
parent
2e7ff4ce30
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7c436704cb
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@ -19,10 +19,10 @@
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%head
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-#%meta(charset="utf-8")
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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/RobotoSlab.css")
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%link(rel="stylesheet" href="fonts/Roboto.css")
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@ -39,8 +39,8 @@
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%body
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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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44. Jahrestagung für Akustik
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%br<>
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@ -82,56 +82,85 @@
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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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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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percussive energy / rhythmic density
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%figure
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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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%p percussive energy / rhythmic density
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%figure.pfifty
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%figcaption Spectrogram <q>James Blunt - You're Beautiful</q>
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%img(src="files/sonagramm_blunt_log.png")
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%figure.pfifty
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%figcaption Spectrogram <q>Decapitated - The Fury</q>
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.clear
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%li
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dynamic distribution
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%figure
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%img.fifty(src="files/decap_envelope.png")
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%figure
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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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%p dynamic distribution
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%figure.pfifty
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%figcaption Dynamic Envelope <q>James Blunt - You're Beautiful</q>
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%img(src="files/blunt_envelope.png")
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%figure.pfifty
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%figcaption Dynamic Envelope <q>Decapitated - The Fury</q>
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%img(src="files/decap_envelope.png")
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%figcaption Dynamic distribution <q>James Blunt - You're Beautiful</q>
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%img(src="files/blunt_dyndist.png")
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-#%figure.pfifty
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%figcaption Dynamic distribution <q>Decapitated - The Fury</q>
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%img(src="files/decap_dyndist.png")
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.clear
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%li
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melodic content / harmonic entropy
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%figure
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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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%p melodic content / harmonic entropy
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%figure.pfifty
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%figure.pfifty
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%figcaption Chromagram <q>Decapitated - The Fury</q>
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.clear
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|
||||
%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
|
||||
Model
|
||||
-----
|
||||
|
||||
RMSE | R<sup>2</sup> | MSE | MAE | r
|
||||
0.64 | 0.80 | 0.40 | 0.49 | 0.90
|
||||
%p(style="text-align:center")<>
|
||||
Sequential feature selection
|
||||
|
||||
* set of 5 features
|
||||
* predictive linear regression model
|
||||
|
||||
%br<>
|
||||
↓
|
||||
%br<>
|
||||
set of 5 features
|
||||
%br<>
|
||||
↓
|
||||
%br<>
|
||||
<b>predictive linear regression model</b>
|
||||
-#
|
||||
RMSE | 0.64
|
||||
R-Squared | 0.80
|
||||
R<sup>2</sup> | 0.80
|
||||
MSE | 0.40
|
||||
MAE | 0.49
|
||||
r | 0.900
|
||||
%figure
|
||||
%img(src="scatter_hardness_model5.png")
|
||||
r | 0.90
|
||||
.clear
|
||||
:markdown
|
||||
Rater Agreement
|
||||
---------------
|
||||
|
||||
Intraclass Correlation Coefficient (Two-Way Model, Consistency): <b>0.653</b>
|
||||
Intraclass Correlation Coefficient <nobr>(Two-Way Model, Consistency): <b>0.653</b></nobr>
|
||||
.clear
|
||||
|
||||
#column1_2
|
||||
-#%section#aims
|
||||
%section#aims
|
||||
%h1 Aims
|
||||
%p
|
||||
The semantic concepts of <q>hardness</q> and <q>darkness</q> 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 <q>hardness</q> and
|
||||
<q>darkness</q> 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 <q>darkness</q> and <q>hardness</q>, 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 <q>hardness</q> (see Intraclass Correlation
|
||||
Coefficients)
|
||||
* For the <q>hardness</q> 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
|
||||
(<nobr>r = 0.65</nobr>, <nobr>p < 0.01</nobr>) 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
|
||||
|
|
@ -225,51 +235,103 @@
|
|||
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
|
||||
<q>brightness</q>** can **not** be confirmed, <q>darkness</q> 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:
|
||||
|
||||
%div(style="text-align:center")
|
||||
%div(style="display:inline-block")
|
||||
:markdown
|
||||
Feature | r | p
|
||||
-----------------------|--------|----------
|
||||
Spectral centroid | 0.3340 | <0.01
|
||||
High frequency content | 0.1526 | 0.0631
|
||||
%figure.fifty
|
||||
-----------------------|-------|----------
|
||||
<nobr>Spectral centroid</nobr> | 0.334 | <0.01
|
||||
<nobr>High frequency content</nobr> | 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
|
||||
<q>harder</q> than those in major mode. (<nobr>p < 0.01</nobr>
|
||||
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")
|
||||
%div(style="display:inline-block")
|
||||
: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
|
||||
RMSE | R<sup>2</sup> | 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<>
|
||||
<b>predictive linear regression model</b>
|
||||
-#
|
||||
RMSE | 0.81
|
||||
R<sup>2</sup> | 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 <nobr>(Two-Way Model, Consistency):
|
||||
<b>0.498</b></nobr>
|
||||
.clear
|
||||
|
||||
%footer
|
||||
%section#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
|
||||
----------
|
||||
|
||||
When comparing <q>darkness</q> and <q>hardness</q>, 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 <q>hardness</q> (see Intraclass Correlation
|
||||
Coefficients)
|
||||
* For the <q>hardness</q> 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
|
||||
(<nobr>r = 0.65</nobr>, <nobr>p < 0.01</nobr>) 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
|
||||
==========
|
||||
----------
|
||||
|
||||
<q>Hardness</q> and <q>darkness</q> constitute perceptually relevant
|
||||
dimensions for a high-level description of music. By decoding the
|
||||
|
|
|
|||
105
style.scss
105
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;
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in a new issue