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Jahrestagung für Akustik %br<> @@ -65,10 +65,19 @@ %main #column1_1 - %section#heavy_features + %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. @@ -81,24 +90,25 @@ %li percussive energy / rhythmic density %figure - %img(style="width:50%" src="files/sonagramm_blunt_log.png") - %img(style="width:50%" src="files/sonagramm_decap_log.png") + %img.fifty(src="files/sonagramm_blunt_log.png") + %img.fifty(src="files/sonagramm_decap_log.png") %li dynamic distribution %figure - %img(style="width:50%" src="files/blunt_envelope.png") - %img(style="width:50%" src="files/decap_envelope.png") + %img.fifty(src="files/blunt_envelope.png") + %img.fifty(src="files/decap_envelope.png") %figure - %img(style="width:50%" src="files/blunt_dyndist.png") - %img(style="width:50%" src="files/decap_dyndist.png") + %img.fifty(src="files/blunt_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(style="width:50%" src="files/decap_chromagram.png") - %section#heavy_model - %h1 Model + %img.fifty(src="files/blunt_chromagram.png") + %img.fifty(src="files/decap_chromagram.png") :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): 0.653 + .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 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. - %figure.right(style="width:50%") - %img(src="files/einhorn/diagramm_vorgang_english.png") - %section#data - %h1 Data - %figure.right(style="width:50%") + .clear + %h2 Data + %figure %img(src="files/scatter_hard_dark_dashedline_2017-09-05.png") - %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}. - - #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. - %section#darkness_features - :markdown - Sound Features - ============== - - Considering Bonferroni correction, 35 significant feature - correlations were found for the darkness ratings. - - While a suspected negative correlation with **timbral - brightness** cannot be confirmed, darkness 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 | <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 - harder than those in major mode. (p < 0.01 - 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 + .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 - Further Results & 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") - %img(src="files/confusionMatrix_simpleTree_genreAgg2.png") + .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** cannot be confirmed, darkness 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 | <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 + harder than those in major mode. (p < 0.01 + 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 Helligkeit 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|< 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 Düsterkeitsbewertung 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|< 0,0001 - RMS Gammatone 4|- 0,3427|< 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 (p < 0.0001 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 Sunn 0)))). - %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 entspannte (relaxed) Stücke eine - niedrigere spektrale Komplexität aufweisen, fröhliche (happy) - Stücke jedoch eine leicht höhere spektrale Komplexität als - nicht fröhliche. - %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| < 0,0001 - HPCP Entropy (mean)| 0,5355| < 0,0001 - Dynamic Complexity| - 0,4855| < 0,0001 - Onset Rate| - 0,4837| < 0,0001 - Pitch Salience| 0,4835| < 0,0001 - MFCC 3 (mean)| 0,4657| < 0,0001 - Spectral Centroid (mean)| 0,3340| < 0,0001 - RMS Energy Gammatone 4| - 0,3427| < 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 - Bestimmtheitsmaß (R2)|0,60 - Mean Squared Error (MSE)|0,65 - Mean Average Error (MAE)|0,64 - 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 Hardness as a semantic audio descriptor for music using automatic feature extraction %span.herausgeber Gesellschaft für Informatik, Bonn - %span.link - %a(href="https://doi.org/10.18420/in2017_06") https://doi.org/10.18420/in2017_06 + %span.link= link 'https://doi.org/10.18420/in2017_06' %li %span.author Grey, J.M. %span.year 1975 diff --git a/style.scss b/style.scss index 23aea22..8f70d9d 100644 --- a/style.scss +++ b/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.pink.svg); + background: url(brushed-metal.dark.svg), url(files/marble_black.png), #252220; 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