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Denis Knauf 2018-07-18 15:56:23 +02:00
parent 2e7ff4ce30
commit 7c436704cb
25 changed files with 387 additions and 175 deletions
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1
.gitignore vendored
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@ -7,4 +7,5 @@ fonts/*.map
style.css style.css
*.swp *.swp
/.bundle /.bundle
/files/einhorn
/vendor/bundle /vendor/bundle

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index.html.haml
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@ -19,10 +19,10 @@
%head %head
-#%meta(charset="utf-8") -#%meta(charset="utf-8")
%title Decoding the sound of 'hardness' and 'darkness' as perceptual dimensions of music %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/Roboto.css")
-#%link(rel="stylesheet" href="fonts/RobotoSlab.css") %link(rel="stylesheet" href="fonts/RobotoSlab.css")
-#%link(rel="stylesheet" href="fonts/PT_Mono.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/Vollkorn.css")
-#%link(rel="stylesheet" href="fonts/Asset.css") -#%link(rel="stylesheet" href="fonts/Asset.css")
-#%link(rel="stylesheet" href="fonts/WithinDestruction.css") -#%link(rel="stylesheet" href="fonts/WithinDestruction.css")
@ -39,8 +39,8 @@
%body %body
%header(style="") %header(style="")
%figure.logos(style="margin-top:0.3cm")<> %figure.logos(style="margin-top:0.3cm")<>
%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") %img#tagungs-logo(style="float:right;height:i3.5em" src="files/icmpc15_logo.png")
-#%div(style="font-size:0.8em;margin-top:1.31cm") -#%div(style="font-size:0.8em;margin-top:1.31cm")
44. Jahrestagung für Akustik 44. Jahrestagung für Akustik
%br<> %br<>
@ -82,56 +82,85 @@
Considering Bonferroni correction, 65 significant feature Considering Bonferroni correction, 65 significant feature
correlations were found for the concept of <q>hardness</q>. correlations were found for the concept of <q>hardness</q>.
The characterizing attributes of <q>hardness</q> include high The characterizing attributes of <q>hardness</q> include **high
tempo and sound density, less focus on clear melodic lines than tempo** and **sound density**, less focus on clear melodic lines than
noise-like sounds and especially the occurrence of strong percussive **noise-like** sounds and especially the occurrence of strong **percussive**
components. components.
%ol %ol
%li %li
percussive energy / rhythmic density %p percussive energy / rhythmic density
%figure %figure.pfifty
%img.fifty(src="files/sonagramm_blunt_log.png") %figcaption Spectrogram <q>James Blunt - You're Beautiful</q>
%img.fifty(src="files/sonagramm_decap_log.png") %img(src="files/sonagramm_blunt_log.png")
%figure.pfifty
%figcaption Spectrogram <q>Decapitated - The Fury</q>
%img(src="files/sonagramm_decap_log.png")
.clear
%li %li
dynamic distribution %p dynamic distribution
%figure %figure.pfifty
%img.fifty(src="files/blunt_envelope.png") %figcaption Dynamic Envelope <q>James Blunt - You're Beautiful</q>
%img.fifty(src="files/decap_envelope.png") %img(src="files/blunt_envelope.png")
%figure %figure.pfifty
%img.fifty(src="files/blunt_dyndist.png") %figcaption Dynamic Envelope <q>Decapitated - The Fury</q>
%img.fifty(src="files/decap_dyndist.png") %img(src="files/decap_envelope.png")
-#%figure.pfifty
%figcaption Dynamic distribution <q>James Blunt - You're Beautiful</q>
%img(src="files/blunt_dyndist.png")
-#%figure.pfifty
%figcaption Dynamic distribution <q>Decapitated - The Fury</q>
%img(src="files/decap_dyndist.png")
.clear
%li %li
melodic content / harmonic entropy %p melodic content / harmonic entropy
%figure %figure.pfifty
%img.fifty(src="files/blunt_chromagram.png") %figcaption Chromagramm <q>James Blunt - You're Beautiful</q>
%img.fifty(src="files/decap_chromagram.png") %img(src="files/blunt_chromagram.png")
:markdown %figure.pfifty
Model %figcaption Chromagram <q>Decapitated - The Fury</q>
----- %img(src="files/decap_chromagram.png")
.clear
Sequential feature selection -#%h2(style="margin-top:1.5em") Model
%h2(style="margin-top:40px") Model
* set of 5 features %figure.fifty.left(style="width:67%;text-align:center")
* predictive linear regression model %img(src="files/scatter_hardness_model5.png")
%div(style="display:inline-block")
RMSE | 0.64 :markdown
R-Squared | 0.80 RMSE | R<sup>2</sup> | MSE | MAE | r
MSE | 0.40 0.64 | 0.80 | 0.40 | 0.49 | 0.90
MAE | 0.49 %p(style="text-align:center")<>
r | 0.900 Sequential feature selection
%figure %br<>
%img(src="scatter_hardness_model5.png") &darr;
%br<>
set of 5 features
%br<>
&darr;
%br<>
<b>predictive linear regression model</b>
-#
RMSE | 0.64
R<sup>2</sup> | 0.80
MSE | 0.40
MAE | 0.49
r | 0.90
.clear
:markdown :markdown
Rater Agreement 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 .clear
#column1_2 #column1_2
-#%section#aims %section#aims
%h1 Aims %h1 Aims
%p %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 Based on computationally obtainable signal features, the creation
of models for the perceptual concepts of <q>hardness</q> and of models for the perceptual concepts of <q>hardness</q> and
<q>darkness</q> in music is aimed for. Furthermore it shall be <q>darkness</q> in music is aimed for. Furthermore it shall be
@ -140,7 +169,7 @@
these dimensions. these dimensions.
%section#method %section#method
%h1 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") %img(src="files/LastFM.png")
%p %p
Based on last.fm listener statistics, 150 pieces of music were selected 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 These ratings served as a ground truth for examining the two
concepts using a machine learning approach: concepts using a machine learning approach:
%figure.right(style="width:50%") %figure.right
//(style="width:50%")
%img(src="files/diagramm_vorgang_english.png") %img(src="files/diagramm_vorgang_english.png")
%p %p
Taking into account 230 features describing spectral distribution, Taking into account 230 features describing spectral distribution,
@ -159,45 +189,25 @@
investigated and combined into models. investigated and combined into models.
Predictors were trained using five-fold cross-validation. Predictors were trained using five-fold cross-validation.
.clear .clear
%h2 Data
-#.blockarrow(style="display:block;width:100%;font-size:6em;margin:0") &#129075;
%section#data(style="margin-top:2em")
%h1 Data
%figure %figure
%img(src="files/scatter_hard_dark_dashedline_2017-09-05.png") %img(src="files/scatter_hard_dark_dashedline_2017-09-05.png")
.blockarrow(style="top:-3.8rem;left:0;right:0") &#129095;
.blockarrow(style="bottom:9rem;left:-3rem") &#129092;
.blockarrow(style="bottom:9rem;right:-3rem") &#129094;
.clear .clear
%section#further_resultes_conclusion %div(style="margin-top:1em;margin-bottom:-1em")
%h1 Further Results &amp; Conclusions %div(style="width:40%;display:inline-block;float:left;text-align:center")
%figure.fifty -#%img(src="files/hammer-306313_960_720.png" style="height:5em")
%img.right(src="files/predictionTree_genreAgg2.png") %img(src="files/thor-hammer3.png" style="height:5em")
%img.right(src="files/confusionMatrix_simpleTree_genreAgg2.png") .blockarrow(style="display:block;width:100%;font-size:7.5rem;margin:0;margin-top:-1.3rem") &#129095;
:markdown %div(style="width:40%;display:inline-block;float:right;text-align:center")
Comparison %img(src="files/Candle.png" style="height:5em")
----------
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 &lt; 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%.
.clear .clear
#column1_3 #column1_3
%section#darkness %section#darkness
%h1 Darkness %h1 Darkness
@ -225,92 +235,144 @@
correlations were found for the <q>darkness</q> ratings. correlations were found for the <q>darkness</q> ratings.
While a suspected negative correlation with **timbral 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 be associated with a high **spectral complexity** and harmonic
traits like **major or minor mode**. traits like **major or minor mode**.
%figure.fifty %figure.fifty.left
%img(src="files/scatter_spectral_centroid_essentia_darkness.png") %img(src="files/scatter_spectral_centroid_essentia_darkness.png")
:markdown %div(style="height:1em")
Correlations between darkness rating and measures for brightness: %p No evidence for negative correlations between darkness rating and measures for brightness:
Feature | r | p %div(style="text-align:center")
-----------------------|--------|---------- %div(style="display:inline-block")
Spectral centroid | 0.3340 | &lt;0.01 :markdown
High frequency content | 0.1526 | 0.0631 Feature | r | p
%figure.fifty -----------------------|-------|----------
<nobr>Spectral centroid</nobr> | 0.334 | &lt;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") %img(src="files/violin_keyEdma_darkMean_blaugelb.png")
%p %p
Musical excerpts in minor mode were significantly rated as Musical excerpts in minor mode were significantly rated as
<q>harder</q> than those in major mode. (<nobr>p &lt; 0.01</nobr> <q>harder</q> than those in major mode. (<nobr>p &lt; 0.01</nobr>
according to t-test) according to t-test)
%h2 Model %h2 Model
%figure.fifty %figure.fifty.right(style="width:67%;text-align:center;margin-bottom:3px")
%img(src="files/scatter_darkness_model8.png") %img(src="files/scatter_darkness_model8.png")
:markdown %div(style="display:inline-block")
Sequential feature selection: :markdown
RMSE | R<sup>2</sup> | MSE | MAE | r
* combination of 8 features 0.81 | 0.60 | 0.65 | 0.64 | 0.798
* predictive linear regression model %p(style="text-align:center")<>
Sequential feature selection
RMSE| 0.81 %br<>
R-Squared| 0.60 &darr;
MSE| 0.65 %br<>
MAE| 0.64 set of 8 features
r| 0.7978 %br<>
&darr;
%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 :markdown
Rater Agreement Rater Agreement
--------------- ---------------
Intraclass Correlation Coefficient (Two-Way Model, Consistency): Intraclass Correlation Coefficient <nobr>(Two-Way Model, Consistency):
**0.498** <b>0.498</b></nobr>
.clear .clear
%footer %footer(style="padding-top:0.2em")
%section#conclusion %section#further_resultes_conclusion(style="padding-bottom:0.20em")
:markdown %h1 Further Results &amp; Conclusions
Conclusion %div
========== #column2_1
:markdown
Comparison
----------
<q>Hardness</q> and <q>darkness</q> constitute perceptually relevant When comparing <q>darkness</q> and <q>hardness</q>, the results
dimensions for a high-level description of music. By decoding the indicate that the latter concept can be more efficiently described
sound characteristics associated with these concepts, they can be and modeled by specific sound attributes:
used for analyzing and indexing music collections and e.g. in a
decision tree for automatic genre prediction.
%section#references * The consistency between ratings given by different raters is
-#(style="width:44.5%;display:inline-block;float:right") higher for <q>hardness</q> (see Intraclass Correlation
%h1 References Coefficients)
%ul.literatur * For the <q>hardness</q> dimension, a model can be based on a more
%li compact set of features and at the same time leads to a better
%span.author Czedik-Eysenberg, I., Knauf, D., &amp; Reuter, C. prediction rate
%span.year 2017
%span.title <q>Hardness</q> as a semantic audio descriptor for music using automatic feature extraction #column2_2
%span.herausgeber Gesellschaft für Informatik, Bonn :markdown
%span.link= link 'https://doi.org/10.18420/in2017_06' Further application
%li -------------------
%span.author Grey, J.M.
%span.year 1975 %figure.fifty(style="width:37%")
%span.title An Exploration of Musical Timbre %img(src="files/confusionMatrix_simpleTree_genreAgg2.png")
%span.herausgeber Stanford University, CCRMA Report No.STAN-M-2 :markdown
%li Although a considerable linear relation
%span.author Li,T., Ogihara,M. (<nobr>r = 0.65</nobr>, <nobr>p &lt; 0.01</nobr>) is present between
%span.year 2003 the two dimensions within the studied dataset, the concepts prove to
%span.title Detecting emotion in music be useful criteria for distinguishing music examples from different
%nobr genres.
%span.herausgeber 4th ISMIR Washington &amp; Baltimore %figure.quarterly(style="clear:initial;width:28%")
%span.pages 239-240 %img(src="files/predictionTree_genreAgg2.svg")
%li %p
%span.author Huron, D. E.g. a simple tree can be constructed for classification into broad
%span.year 2008 genre categories (Pop, Techno, Metal, Gothic) with an accuracy of
%span.title A comparison of average pitch height and interval size in major-and minor-key themes 74&nbsp;%.
%nobr
%span.herausgeber Empirical Musicology Review, 3 #column2_3
%span.pages 59-63 :markdown
%li Conclusion
%span.author Siddiq,S. et al. ----------
%span.year 2014
%span.title Kein Raum für Klangfarben - Timbre Spaces im Vergleich <q>Hardness</q> and <q>darkness</q> constitute perceptually relevant
%nobr dimensions for a high-level description of music. By decoding the
%span.herausgeber 40. DAGA sound characteristics associated with these concepts, they can be
%span.pages 56-57 used for analyzing and indexing music collections and e.g. in a
.clear 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., &amp; Reuter, C.
%span.year 2017
%span.title <q>Hardness</q> as a semantic audio descriptor for music using automatic feature extraction
%span.herausgeber Gesellschaft für Informatik, Bonn
%span.link= 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 &amp; 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

105
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