- require "base64" - def quellen opts - etc = opts.key? :etc - if etc - opts.delete :etc - etc = "\n" - else - etc = '' - "#{opts.map {|k, v| "#{k}" }.join "\n"}#{etc}" - def link link - "#{link}" - def import_data file - 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 -#%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/PT_Mono.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") -#%link(rel="stylesheet" href="fonts/BlackDahlia.css") -#%link(rel="stylesheet" href="fonts/ThroughStruggleDEMO.css") -#%link(rel="stylesheet" href="fonts/TheDefiler.css") %link(rel="stylesheet" href="fonts/Cardo.css") %link(rel="stylesheet" href="fonts/Italianno.css") -#%link(rel="stylesheet" href="fonts/CinzelDecorative.css") %link(rel="stylesheet" href="style.css") %meta(name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no") %body %header(style="") %figure.logos(style="margin-top:0.3cm")<> %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<> Technische Universität München %br<> 19. März 2018 .. 22. März 2018 -#.grabstein .grabstein-was DAGA .grabstein-wo Technische Universität München .grabstein-von ✦ 19. März 2018 .grabstein-bis ✝ 22. März 2018 -#%img(style="height:7cm;top:3cm;right:24cm;position:absolute" alt="Dunkle Nacht" src="files/Candle.png") %h1 Decoding the sound of hardness and darkness as perceptual dimensions of music %p#authors<> %span.author(data-mark="1,2")<> Isabella Czedik-Eysenberg %span.author(data-mark="1")<> Christoph Reuter %span.author(data-mark="2")<> Denis Knauf %p#institutions<> %span.institution(data-mark="1")<> University of Vienna, Austria %span.institution(data-mark="2")<> Student at Technical University of Vienna, Austria %main #column1_1 %section#hardness %h1 Hardness %p Hardness is often considered a distinctive feature of (heavy) metal music, as well as in genres like hardcore techno or Neue Deutsche Härte. In a previous investigation the concept of hardness 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 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** components. %ol %li %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 %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 %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 -#%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 .clear #column1_2 %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 explored if there are interactions between the two factors and to which extent it is possible to classify musical genres based on these dimensions. %section#method %h1 Method %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 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 hardness and darkness. 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. .clear -#.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 %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 %p Certain kinds of music are sometimes described as dark in a metaphorical sense, especially in genres like gothic or doom metal. According to musical adjective classifications dark is part of the same cluster as gloomy, sad or depressing #{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 dark timbre #{quellen Huron: 2008}. In timbre research brightness is often considered one of the central perceptual axes #{quellen Grey: 1975, 'Siddiq et al.' => 2014}, which raises the question if darkness in music is also reflected as the inverse of this timbral brightness concept. :markdown Sound Features -------------- Considering Bonferroni correction, 35 significant feature correlations were found for the darkness ratings. While a suspected negative correlation with **timbral 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.left %img(src="files/scatter_spectral_centroid_essentia_darkness.png") %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 | 0.334 | <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.right(style="width:67%;text-align:center;margin-bottom:3px") %img(src="files/scatter_darkness_model8.png") %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 .clear %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 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 #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