A Music and Lyric Analysis of 259 Finalists from the Eurovision Song Contest 2010-2019
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Songs for Europe: a music and lyric analysis of 259 finalists from the Eurovision Song Contest 2010-2019 Prof Joe Bennett (Berklee College of Music, USA), Simon Troup (Digital Music Art, UK) Abstract: This corpus study provides an overview of the common musical and lyric characteristics of songs from the Eurovision Song Contest (ESC) during the past decade. It uses as its dataset 259 songs, comprising the 25-27 finalists in each year’s Grand Final. Hard data includes high-level musical characteristics such as tempo, key, tonality, dynamic range, and melodic pitch range. Soft data includes lyric themes and musical style. Song styles were categorized according to six ‘archetypes’, adapted from existing categories suggested by previous ESC journalism and academic writing.1 Lyric themes were categorized according to six core themes identified by the researchers through detailed listening and text analysis. The full corpus was analysed, to discuss common musical and lyric characteristics of each archetype, and high and low-scoring subgroups were compared, to investigate whether there are common music or lyric characteristics that correlate with success (measured in numbers of aggregated points per song). Methodology included computer-aided audio analysis (BPM detection, dynamic range), subjective listening (archetype and lyric classification), music analysis (key centre, tonality) and statistical analysis of the dataset. The research, which is an ongoing project 2020-2021, aims to identify the high-level characteristics (of tonality, archetype, lyric theme, dynamic range, tempo etc) exhibited by finalists, and to investigate any characteristics that differentiate winners and other high- scoring songs. 2 1 Jess Carniel, “‘Schlager’, Scandi-Pop and Sparkles: Your Guide to the Musical Styles of Eurovision,” The Conversation, May 9, 2018, http://theconversation.com/schlager-scandi-pop-and-sparkles-your-guide-to-the-musical-styles-of-eurovision-96268. 2 This document is currently © Joe Bennett and Simon Troup, 2020. Licences for third party usage are in progress. Images are offered under CC BY and may be used freely with attribution. Contact via joebennett.net. Page 1 of 28 About the project The initial research was commissioned by Netflix, in celebration of the Eurovision Song Contest, and as part of the promotion for the 2020 movie “Eurovision Song Contest: The Story of Fire Saga” starring Will Ferrell and Rachel McAdams. In addition to the corpus study, the researchers analysed several songs from the movie, and also from the cancelled 2020 contest, to enable speculation about how these would fare in a real Eurovision final (results published separately). At the time of writing (June 2020), the full ten-year dataset is complete; this preliminary document outlines some early findings and tentative conclusions about the characteristics of the ‘Eurovision formula’. We intend to publish more findings, and expand the dataset, in 2021 and beyond. We would welcome approaches from individual music scholars, statistics experts and music or media organizations that may wish to help us to develop the work. Data visualisations are provided at tinyurl.com/eurodownload, and may be used freely under Creative Commons ‘Attribution CC BY’ terms. Credit: Digital Music Art / Joe Bennett Page 2 of 28 Introduction The purpose of the research was to attempt to codify music and lyric characteristics of Eurovision finalists, in order to learn more about massed listener preferences across the region. We wished to test the hypothesis that a large number of music preferences from different cultures might lead to a cultural centre ground - a shared pan-European musical sense (or a cultural dilution, depending on one’s point of view), expressed in song. We focused exclusively on music and lyrics, deliberately ignoring other factors such as performance staging, performer demographics, judging mechanisms3, or geo-political concerns4, and we acknowledge prior research that has shown how these factors may also affect the outcome of the contest. We chose to analyse the years 2010-2019, to achieve a balance between a viable project scale and meaningful statistical outcomes, and also to ensure that we were making inferences about the modern state of Eurovision, as opposed to the sound of earlier decades. To create a manageable corpus of songs, and to allow for pre-filtering of extreme outlier songs that voting audiences had easily rejected, only songs from the Saturday night final were selected (25-27 songs per year). Methodology The list of song titles, participants, country, performer, scores, running order and scores was sourced from eschome.net and eurovision.tv. Audio was sourced from youtube.com. For each song, our ideal choice was the home country’s ‘official video’ via eurovision.tv; if this was unavailable, we used the TV show recording from the performance in the final; if that too was unavailable, we used any other source e.g. technical rehearsal, streaming services, or lyric-only videos. The full list of data categories is available in Appendix 1. The researchers immersed themselves in the full 259-song corpus (Appendix 2) for an intensive initial 15-day period, listening to the songs more than 10 times each, and isolating short sections for discussion/analysis. Special attention was given to the top-scoring songs in order to try to gain a deeper understanding of the musical and lyric characteristics that voters were consistently favouring. This process, combined with historical and academic research into Eurovision’s origins 5 6 7 8, led to 3 Derek Gatherer, “Comparison of Eurovision Song Contest Simulation with Actual Results Reveals Shifting Patterns of Collusive Voting Alliances.,” Journal of Artificial Societies and Social Simulation, 2009, 14. 4 Daniel Fenn et al., “How Does Europe Make Its Mind Up? Connections, Cliques, and Compatibility between Countries in the Eurovision Song Contest,” Physica A: Statistical Mechanics and Its Applications 360, no. 2 (February 2006): 576–98, https://doi.org/10.1016/j.physa.2005.06.051. 5 Fabian Leach, “Eurovision Song Is Not Pop - How National Identity in Eurovision Defines the Function of the Eurovision Song” (Masters Thesis, Utrecht, Utrecht University, 2018). 6 Eurovision.tv, “Eurovision Facts & Figures,” Eurovision.tv, January 12, 2017, https://eurovision.tv/about/facts-and-figures. 7 Eurovision.tv, “Eurovision History by Year 1956-2019,” Eurovision.tv, 2020, https://eurovision.tv/events. 8 Filip Bolman, “The Politics of Power, Pleasure and Prayer in the Eurovision Song Contest,” Muzikologija, no. 7 (2007): 39–67, https://doi.org/10.2298/MUZ0707039B. Page 3 of 28 the style/tempo archetypes and lyric themes that appeared consistently. Each song was then tagged with its lyric theme (love song, dance party, history etc), and archetype category (Ballad, Anthem, Euro-pop etc). These categories are discussed in the next section. The topline melody of each song was analysed, in its harmonic context, and tagged with a broad tonality MAJOR or MINOR; if there was consistent use of a particular scale or mode throughout the song’s duration, this was also included e.g. MINOR (Aeolian) or MAJOR (Phrygian Dominant). One of the common-knowledge musical clichés about Eurovision songs is that they often modulate to a higher key towards the end. To investigate the prevalence of this phenomenon in contemporary Eurovision, we recorded the size of any key changes in semitones. Key change data was only recorded for modulations that occurred towards the end; songs that had key changes built into their composition (e.g. when the verse and chorus were in different keys) were not considered as modulating. All chorus key changes were pitch-positive throughout the corpus - no song ever went to a lower-pitched key. Computer-aided analysis was deployed for dynamic range, in order to measure the extent to which Eurovision songs start quietly and build to a dynamic climax. After all audio was normalized to 0dB, a 5-second excerpt of each song’s audio file was sampled at 0:10 from the beginning (assumed to be the intro or part of verse 1), and another 5-second excerpt taken 0:20 from the end of the file (assumed to be the final chorus or outro). These two 5s excerpts were combined to make a single 10-second audio clip. The difference between the average RMS level (i.e. loudness) of the first and second half of this file was then measured using the Contrast function in Audacity software, resulting in a simplified dynamic range number that represented the ‘build’ of the song between the start and end. Figure 1: typical Ballad comparison waveform (from Kruna, Serbia, 2019), showing two 5-second excerpts (left side=intro; right side=outro). The study is currently limited to high-level characteristics that apply to the whole song. Further work could include deeper analysis of lyric, song form, dynamics, melody, harmony, and instrumentation, supported by MIR (Music Information Retrieval) techniques. Page 4 of 28 The Six Archetypes of Eurovision We based our initial song categorizations on the Eurovision archetypes model proposed by Australian cultural scholar Jess Carneil.9 These were refined with the addition of tempo-specific, lyric-based, and song arrangement parameters. BPM was the first/main criterion for classification, followed by instrumentation, lyric theme, instrumentation, and tonality. We ignored any non-musical parameters (e.g. costume, gender, nationality or age of the performers). Although most of the Carneil models were readily recognizable in the corpus, we added an additional category ‘Anthem’ to cover mid-tempo (85-119 BPM) mid-dynamic songs with powerful choruses; Anthems are situated midway between the tempo and dynamic poles of slow Ballads and fast Euro- pop. Songs that did not easily fit the six archetypes were classified as ‘niche’, and their style was noted as an additional parameter (e.g. punk, indie-rock, reggaeton). Niche songs were in the minority; 84% of the 259 songs in the corpus conformed to one of the six archetypes.