Musical Aspects of Popular Music an Analysis of the Dutch Top 40 Throughout the Years

Musical aspects of popular music An analysis of the Dutch Top 40 throughout the years

Name: Joost Verboven ANR: 217675 Bachelor/master thesis: Master thesis Study: Communication- and Information Sciences Specialization: Business Communication and Digital Media Faculty: School of Humanities Institution: Tilburg University, Tilburg

Supervisor: Dr. M.M. Van Zaanen Second reader: Dr. M.J.M. Hoondert

August, 2014 Table of contents

Chapter 1: Introduction ...... 4

1.1 Context ...... 4

1.2 Relevance ...... 5

1.3 Problem statement...... 6

1.4 Methodology...... 6

Chapter 2: Background literature ...... 7

2.1 Defining popular music ...... 7

2.1.1 The sixties ...... 8

2.1.2 The seventies ...... 9

2.1.3 The eighties ...... 9

2.1.4 The nineties ...... 10

2.1.5 The twenty-first century ...... 11

2.2 Development of popular music ...... 11

2.3 The Echo Nest and its meta data ...... 12

Chapter 3: Experimental setup ...... 15

3.1 Materials ...... 15

3.2 Procedure ...... 15

3.2.1 Pre-tests ...... 16

3.2.2 Comparing musical aspects over periods ...... 16

Chapter 4: Results ...... 18

4.1 Pre-tests ...... 18

4.1.1 Differences in musical aspects ...... 18

4.1.2 Correlation ...... 22

4.2 Comparing musical aspects over periods ...... 24

4.2.1 Song hotttnesss ...... 24

4.2.2 Artist hotttnesss ...... 24

4.2.3 Artist familiarity...... 25

4.2.4 Energy ...... 26

4.2.5 Liveness ...... 27

4.2.6 Speechiness ...... 27

4.2.7 Acousticness ...... 28

4.2.8 Valence ...... 29

4.2.9 Danceability ...... 30

4.2.10 Tempo ...... 30

4.2.11 Loudness ...... 31

Chapter 5: Discussion and conclusion ...... 33

5.1 Pre-experiment tests ...... 33

5.2 Comparing musical aspects over periods ...... 34

5.3 Limitations and future work ...... 36

References ...... 38

Chapter 1: Introduction "Popular music formed the soundtrack of my life" (Scorsese, n.d.).

This thesis contains an analysis of popular music, which might be extremely important in one's life, as Martin Scorsese, the famous producer, director and actor ("Martin Scorsese", n.d.) mentions above. This research starts with an introduction, in which the context (section 1.1), relevance (section 1.2), problem statement (section 1.3) and short methodology of this research (section 1.4) are described.

1.1 Context The word 'popular', when used as an adjective, can be defined as a phenomenon which is appreciated by a large audience or the general public (Shuker, 2001). He translates this definition into the world of media; hence, it means that, for example, movies, television programs, books and music are massively consumed, and that 'popularity is indicated by ratings surveys, box-office returns and sales figures'. The Dutch Top 40, which is the topic of research in this thesis, is the most complete, weekly overview of actual popular music in The Netherlands: it takes into account regular physical sales of music, as well as legally downloaded music. Furthermore, since 2008, the Dutch investigation bureau De Vos & Jansen inquires 500 variable participants of a music panel to name their most popular songs. Therefore, ratings surveys, which are named by Shuker (2001) in his definition of 'popular', are taken into account in composing The Dutch Top 40. This music chart started in 1965 and runs up until the present day ("Geschiedenis Nederlandse Top 40," n.d.). Therefore, in order to analyze musical aspects of popular music within the Netherlands throughout the years, the Dutch Top 40 is the most appropriate set of songs to use. It provides a large database based over 48 years and popularity is indicated, as Shuker (2001) already mentioned as being important, by ratings surveys and sales figures. We can distinguish multiple eras in which a particular type of music was considered to be dominant, for example rock 'n roll (mid-1950's), disco, glam rock, hard rock, electronic music (1970's), gangsta rap (1980's) and rave (1990's) (Bennett, 2007; Martin & Seagrave, 1988; Cloonan, 1996; Winfield & Davidson, 1999, Cohen, 1997). This thesis aims to identify trends in certain aspects of music over a long time span, namely 1965 to 2014. The problem statement which is being investigated in this research will be elaborated in the coming sections.

1.2 Relevance This thesis is relevant for a number of issues. First, musical aspects might be important for musicians or record labels who want to score a hit in the Netherlands. As Hendricks & Sorensen (2009) mentioned, consumers face: An overwhelmingly large and constantly growing choice set [...] However, only a small fraction of these products turn out to be profitable. Even among the profitable products, the distribution of returns is extremely skewed: a large share of the total industry profit is claimed by a small number of very successful products. (pp. 324-325) Eliot (1989) emphasizes this statement by noticing that a band still has a small chance of breaking even, let alone making a profit, even when signed by a major record label. Furthermore, individuals now have the largest set of songs available (compared to the past), due to digital technologies (McFee, Bertin-Mahieux, Ellis & Lanckriet, 2012) such as computers and downloading services, which implies that consumers nowadays have more control over the songs which one wants to listen. Therefore, artists should meet the needs of consumers more than ever when one wants to make profit. Therefore, if, in this thesis, certain important musical aspects of scoring a hit are being distinguished, it might be the case that musicians or record labels want to adjust their future records in order to stand out from the mass or to blend in with the current popular flow in music. As Shuker (2001) stated, the range of producing varies from 'try and see what happens' to a 'more calculated, entrepreneurial attitude'. Due to this sliding scale, one may want to adjust their producing style in order to maximize the chances of scoring a hit. If a producer or artist opts for the entrepreneurial approach in a transitional phase between two genres and can be considered as a predecessor of other artists, it might be that these predecessors can earn a lot of money when the new genre proves to be popular. Lastly, research on analyzing popular music over the years, based on musical aspects and based on a large dataset has not been conducted. Bertin-Mahieux, Ellis, Whitman & Lamere (2011) endorse this statement by concluding that there has been a lack of a large music collection which covers a wide range of genres (at least within western pop) and a long time span. Therefore, this thesis aims to provide a method for other researchers who want to investigate similar phenomena.

1.3 Problem statement The problem statement which will be investigated in this research is: How do musical aspects in popular music change over a certain period in the Netherlands? In order to answer the problem statement, three research questions are being discussed: 1. Do musical aspects of popular music change during a year? 2. Do these musical aspects of popular music change parallel to the musical periods which we typically identify? 3. Which musical aspects of popular music did change during the period 1965-2014? Since no earlier research has been found, the relevant aspects will, or will not, stand out during the research. Therefore, no hypotheses are posed.

1.4 Methodology In order to comprehend the setup of this research, a short overview of the method will be given beforehand. The complete experimental setup will be elaborated in chapter 3. The first step which needs to be taken in this research, is desk research: in order to elaborate a problem statement, relevant literature needs to be analyzed and will be described in chapter 2. Next, the data of all Dutch Top 40s, from the start in 1965 up until present day, is collected (song title, artist, number of weeks present in the Dutch Top 40 and highest rank). Hereafter, additional meta data has to be collected and analyzed. The results of the analysis of musical aspects are described in chapter 4, whereas the discussion, conclusion and limitations and future work cover chapter 5.

Chapter 2: Background literature This chapter provides an overview of history of popular music by defining popular music, as well as the adjustment of popular music. Furthermore, The Echo Nest is described; this database is used in this research to gather metadata of songs which have had a ranking in The Dutch Top 40.

2.1 Defining popular music Shuker (2001) states that popular music needs a precise, straightforward definition. However, earlier research has shown multiple definitions of popular music, which are discussed below. Middleton (1990) concluded that the term popular music is complex, since clear criteria on the term popular music are not established. Therefore, one is tempted to use the legendary notion of folk songs: songs which are sung by people and are popular with someone. Furthermore, Shuker (2001) finds that the term popular music is open for debate; for instance, Clarke (1990) mentions that popular music is divided in mainstream genres which have a vast amount of tributaries, such as jazz, ragtime, blues, rhythm and blues, country, rock (and rock ’n’ roll and rockabilly), pub rock, punk rock, acid rock, heavy metal, bubblegum, and reggae. However, Sinclair (1992) finds the commercial nature of popular music, regardless of its genre, as being most important, whereas Shuker (2001) states that popular music is produced for a large audience or the general public and that popularity can be measured through ratings surveys, box-office returns and sales figures. Adorno (1941), as cited in Shuker (2001), emphasized the fundamental characteristic which he perceived: popular music is standardized, even when one wants to evade this standardization. However, Adorno (1976), as cited in Shuker (2001), still related popular music to Tin Pan Alley (i.e. American popular music in the 1920s to 1940s, characterized by simple rhyming and harmonies) and jazz-oriented versions of it. The claim of Adorno, therefore, did not stand and shows that musical aspects might change over time. Balkwill & Thompson (1999) endorse this statement by concluding that music listeners might be influenced by variations in psychophysical aspects of music, for example tempo and timbre. In this research, the definition of Shuker (2001) is leading: popular music is music which is produced for a large audience or the general public, and popularity can be measured via sales figures and ratings surveys. The definition of Shuker contains two key elements: A) pop(ular) music is produced for large audience, and B) popular music is measured via sales figures and ratings surveys. Modern popular music can be categorized in periods; here, the sixties, seventies, eighties, nineties and the twenty-first century are being discussed. However,

7 the emphasis in twenty-first century popular music lies on the 2000s, since hardly any literature on popular music in the 2010s has been found, probably due to the fact that this era is still running.

2.1.1 The sixties In the sixties, several worldy musical developments are distinguished. The People History (n.d.-a) discusses 'the British invasion', 'Motown/R&B', 'surf rock and psychedelic rock', 'roots rock and hard rock' and 'folk rock and protest music'. The British invasion in hit lists considers the first half to the mid-sixties, in which several British artists scored major mainstream hits worldwide. Artists who were responsible for this invasion include The Beatles, The Rolling Stones, The Who and Tom Jones. The bands often started off covering American hit songs in which rock 'n roll and R&B influences were used. When these bands became popular, they started producing new music in order to create own styles. The Motown/R&B development was of major significance for the Civil Rights movement and integration into the American society. Motown, a record label based in Detroit, consisted of mostly Afro-American artists, including Diana Ross and The Supremes, Marvin Gaye and Stevie Wonder. Music made by colored artists became a worldwide success, which was conveyed by R&B artists as well. Taking surf rock and psychedelic rock together, these genres were popular during all of the sixties. Surf rock was mainly popular in the early to mid-sixties and considered songs about surfing, cars and girls. The most famous band in this genre is The Beach Boys; other bands are The Ventures and The Champs. Psychedelic rock was more popular in the late sixties and was associated with hippies and drug use; psychedelic rock was used to enhance the trips of drug users. The texts were often strange and referred to drug use as well. Popular artists in this genre were The Doors, The Jimi Hendrix Experience and Pink Floyd. Roots rock was popular in the mid to late sixties and was based on elements of folk music, blues, country and rock 'n roll. The genre was considered to be 'back to basic' and artists like Creedence Clearwater Revival, Bob Dylan and Roy Orbison contributed to the popularity of this genre. Hard rock was a follow-up of rock 'n roll and took its elements to make the music more heavy. The style became popular amongst rebellious youth and some artists destroyed their instruments on stage. Hard rock artists which became famous are, amongst others, Led Zeppelin, Deep Purple and The Rolling Stones.

During the mid-sixties, folk rock became popular as well. This genre was melodic and did not necessarily carry out a protest message; however, the majority of folk rock did contain this message. Bob Dylan and Peter, Paul and Mary are artists who played in this scene. Protest music was different when compared to folk rock, since it always carried out a protest message. These messages were about news events, social injustice or cultural changes. No particular artists were assigned to this genre, since many mainstream artists of many different genres wrote protest songs as well.

2.1.2 The seventies The seventies contained four major worldy genres, according to The People History (n.d.-b): disco/club, progressive rock, punk rock/new wave and funk & soul. Disco/club music defined the era of popular music in the seventies. It contained music to which we still dance today. However, its popularity in the seventies was relatively short due to the fact that it ignored musical significance by commercialization. Furthermore, society considered it to be 'silly' music. Major artists in this genre were The Village People, The Bee Gees and Gloria Gaynor. Progressive rock was a genre which combined rock music with other styles, such as opera or classical music. The artists in this genre experimented with songs on stage and the songs lasted longer than songs in most of the other genres. Albums in this genre were often 'concept albums', which followed a theme during the whole album. Popular artists in progressive rock were Genesis, Queen and Supertramp. The third genre, punk rock/new wave, was a follow-up of sixties music but was considered to be more upbeat and harder. Heavy guitar riffs and decibels are characteristic in this genre, whose music was produced by, amongst others, The Ramones, Sex Pistols and Blondie. Funk & soul is the last main genre in the seventies and stem from R&B, jazz and soul music of the late sixties and was characterized by more beats and psychedelic tones. Danceable music, extravagant costumes and outrageous personalities characterizes funk music as well. James Brown is considered to be the creator of funk & soul and artists like Kool & The Gang and The Jackson Five played in this scene as well.

2.1.3 The eighties The People History (n.d.-c) described the eighties in four main genres, namely pop, hip hop and rap, new wave and hair metal.

Pop music changed during the eighties due to MTV and a larger influence of an artist's image. Pop stars were set as icons of a new genre and songs which were produced by major stars like Michael Jackson, Madonna and Whitney Houston were set as a gold standard for what pop music should be like. The pop music genre can be considered as a broad genre, since old pop stars such as Paul McCartney and Lionel Richie re-emerged into popularity. Pop music produced one hit wonders as well, such as Nena (99 Luftballons) A-Ha (Take on me) and Dexy's Midnight Runners (Come on Eileen). Hip hop and rap music originated in Afro-American communities and became more mainstream in the late eighties, when MTV started showing music videos from colored artists. This genre is characterized by samples from old songs, rapping lyrics and beat boxing and popular artists in hip hop and rap are LL Cool J, Run DMC and Beastie Boys. New wave began in the late seventies and remained popular until the mid-eighties. Its sound was related to punk rock, dance music, synthesizers and other electronic instruments. Major artists in this scene were Duran Duran, Billy Idol and The Talking Heads. Hair metal followed up glam rock which started in the seventies and contained elements of heavy metal, punk rock and traditional rock music. Songs often described a theme (e.g. drugs or women) and were often party anthems or power ballads. Hair metal is called hair metal because the mostly male-dominated bands had extravagant haircuts with long, big- styled hair. Bon Jovi, Van Halen and Aerosmith are artists who became famous in this genre.

2.1.4 The nineties In the nineties, genres like hip hop and rap, pop and R&B had a revival or remained popular. Furthermore, grunge, country pop and a new British invasion were leading (McDonald, n.d.). In The Netherlands, 'gabber' music, which includes samples and synthesized melodies with approximately 150 to 220 beats per minute, was popular as well (Last.fm, n.d.-a). In the nineties, hip hop and rap had a golden age. In the eighties, the music transformed from party songs to political songs and although it faced a storm of negative press, the rap scene became more and more popular. Most of rap's biggest stars released their music in this era, such as Ice-T, Ice Cube and Tupac Shakur (Ranker, n.d). The pop genre, which had already been described in section 2.1.3, had a revival in the nineties, which was now characterized by boy and girl bands such as Spice Girls, Backstreet Boys and *NSYNC. Furthermore, R&B, which has been described in section 2.1.1, had sale records which not had been realized since the sixties with stars like Mary J. Blige, Boyz II Men and Tony Braxton.

A new genre in the nineties was grunge, which defined the decade next to rap music. Grunge was inspired by, amongst others, hardcore punk and metal and can be characterized by electric guitars, contrasting song dynamics and apathetic or angst-filled lyrics (Last.fm, n.d.-b). Nirvana, Pearl Jam and Foo Fighters are main artists in this genre (Last.fm, n.d.-c). Country pop is pop music which has country elements included. Artists such as Alan Jackson and Shania Twain took country pop into the hit lists. A new British invasion, which was preceded by the first British invasion (described in section 2.1.1), was leading in the nineties hit lists as well with artists like The Verve, Blur and Pulp.

2.1.5 The twenty-first century In the 2000s, hip hop and rap music remained a popular genre (The People History, n.d.-d). It is characterized by violent and sexual lyrics, but not all rap music contains these themes. Artists like Eminem, Usher and Kanye West combined rap and hip hop, which provided songs which have an enjoyable rhythm and rap lyrics. Related to rap music is rock metal rap, which combines rap lyrics and style with the genres rock and metal. Kid Rock, Limp Bizkit and Linkin Park are artists who represent this genre. Pop music revived in the 2000s as well due to shows such as Pop Idol, with Kelly Clarkson as the most famous example. Other famous pop artists (for example Madonna) remained popular as well. Next to rap, hip hop and pop music, emo music is a genre which developed in the 2000s. Emo music is distinguished by heartfelt lyrics and melancholic melodies. Lyrics sometimes refer to the government and the administration. Artists who represent this scene are Green Day and Dixie Chicks.

2.2 Development of popular music Popular products are mainly produced for mass markets, while few large companies dominate this market. In music, record labels such as EMI, BMG, Sony Music Entertainment, Warner Music Group and Universal/Polygram are the main competitors (Shuker, 2001). These companies are driven by capitalism: striving to maximize the profit of the products they offer. Since, as said before, a small fraction of offered products (for example, music) can turn out to be profitable (Hendricks & Sorensen, 2009), musical aspects should be a major indicator of research to this companies in order to try to score as many hits as possible. Furthermore, Eliot (1989) mentioned that a band has a small chance of breaking even, let alone making a profit, even when signed by a major record label. Opposing this statement are Barnett & Cavanagh

(1994), who state that the powerful music industry, driven by capitalism, can manipulate and create markets for their music. This shows that two conflicting flows in research can be distinguished: corporate power as the main factor, and consumer's power as the main factor. Furthermore, a third, middling flow has been established: an interaction between corporate power and consumer's power in the music industry. Consumption and production do not act in isolation, but are engaged in a dialectic (Shuker, 2001). Furthermore, Shuker states that music is studied in different forms such as political economy and cultural, feminist and media studies. However, it again shows that no concrete, pure research on (possibly changing) musical aspects of popular music has been conducted.

2.3 The Echo Nest and its meta data For this research, aspects of popular music needed to be analyzed. Therefore, additional data was collected from The Echo Nest. The Echo Nest is a web service which provides a spectrum of musical aspects when given the name of the artist and/or song (Corthaut, Govaerts, Verbert & Duval, 2008). Bertin-Mahieux et al. (2011) provide a similar definition regarding the web service: it collects meta data and provides an audio analysis, based on a database of millions of songs. It is important to mention that all gathered data is openly accessible, which follows research of McFee et al. (2012) who state that a lack of open, transparent data has prevented academic research to investigate personalizes recommendation on music. The conclusion of McFee et al. (2012) is applicable to the current research as well. The Echo Nest can provide a number of aspects of songs. The gathering of salient features of songs is needed, in order to analyze possible popular aspects over a long period of time. In table 1, musical aspects which may be relevant for a hit song are described. The first column shows the musical aspect, whereas the second column shows the respective explanations. However, since this research may be regarded as explorative, other musical aspects might have influence as well.

Table 1 Musical aspects The Echo Nest

Musical aspect Explanation Song hotttnesss The hotttnesss of a song ranges from zero to one and represents how hot or trending a song is at that moment in time (The Echo Nest, n.d.-a). A value closer to one represents a higher song hotttnesss Artist hotttnesss The hotttnesss of an artist shows how hot or trending the artist of a song is in a particular moment in time (The Echo Nest, n.d.-a), ranging from zero to one. A value closer to one represents a higher artist hotttnesss. Artist familiarity Ranging from zero to one, artist familiarity shows how well-known the artist of a song is. A rating closer to one represents a larger change of an artist being recognized when randomly being asked to a person (Lamere, 2009). Energy Energy is a perceptual aspect which measures intensity and powerful activity in a song, ranging from zero to one (a value closer to one represents more energy). Energetic songs mostly sound fast, loud and noisy. Features which contribute to a song's energy are dynamic range, perceived loudness, timbre, onset rate and general entropy (The Echo Nest, n.d.-b). Liveness Measures the presence of an audience in a recording. This aspect ranges from zero to one, with a value closer to one represents a more likely presence of an audience. A value above 0.8 means a high likeliness of an audience being present, whereas values between 0.6 and 0.8 may represent song which contain simulated audience sounds at the beginning and/or end of a song. Values below 0.6 most likely represent studio recordings (The Echo Nest, n.d.-b). Speechiness As the name states, speechiness measures the number of spoken words in a song, ranging from zero to one. However, values above 0.66 represent recordings which are based on text only. Values between 0.33 and 0.66 may describe music combined with spoken language (for example rap music), whereas values ranging from zero to 0.33 most likely represents music in which has been (or has not) been sung (The Echo Nest, n.d.-b). Table 1 (continuation) Musical aspects The Echo Nest

Musical aspect Explanation Acousticness Ranges from zero to one, with a value closer to one represents a higher acousticness. Acoustic songs mainly contain orchestral instruments, an unaltered voice, acoustic guitars and natural drum kits, whereas less acoustic songs contain, for example, synthesizers, electric guitars and amplified instruments (The Echo Nest, n.d.-b). Valence This aspect describes the musical positivity of a song, ranging from zero to one. Songs which have a rating closer to one represent more positivity (e.g. happy, cheerful), whereas songs with a rating closer to zero represent more negativity (e.g. sad, angry). However, in case of vocal music, the lyrics may differ from the acoustic mood (i.e. valence) which a song represents (The Echo Nest, n.d.-b). Danceability The danceability of a song is defined as how suitable a track is for dancing. The value ranges from zero to one, in which a rating closer to one defines a larger danceability. This musical aspect is consists out of tempo, rhythm stability, beat strength and overall regularity (The Echo Nest, n.d.-b). Tempo Tempo represents the overall estimated tempo of a track in beats per minute (The Echo Nest, 2014). The more beats per minute, the higher the speed or pace of a song. Loudness Loudness is measured in decibels, with an overall average of a track (The Echo Nest, 2014). The higher the rating, the more average decibels a song contains.

Chapter 3: Experimental setup In short, the method of the research has been described in section 1.4. However, in order to conduct and reproduce this research, a description of the complete experimental setup is needed. Therefore, materials (section 3.1) and procedure (section 3.2) are discussed below. No literature on longitudinal aspects of popular music has been found, so this thesis can be considered as explorative research.

3.1 Materials The dataset which has been used in this research consisted of A) all songs which have had a listing in the Dutch Top 40 since it started in 1965 and B) musical data of these songs provided by The Echo Nest. The database contained 101,706 songs which cover all Dutch Top 40s from the start in 1965 until the first of March 2014. All songs can be found on the website of the Dutch Top 40 (www.top40.nl). Categories which have been included in the downloaded dataset from the website of the Dutch Top 40 are: song name, artist name, number of weeks and highest rank. McFee, Bertin-Mahieux, Ellis and Lanckriet (2012) concluded that the heart of collecting music data lies in meta-data which allows you to identify a song. Therefore, the names of the artist and song are a good starting point. Researchers could then gather all needed information in order to conduct their research. Data regarding number of weeks and highest rank has been included for possible future research. Furthermore, The Echo Nest provided additional data, which has been described in table 1. A script was written in order to send information regarding the songs of The Dutch Top 40s to The Echo Nest, which then provided the additional meta data of the songs.

3.2 Procedure First, information (including the names of artist and song) of all songs which have had a rating in the Dutch Top 40 has been downloaded. Next, all songs where inserted in The Echo Nest API, which provided detailed information on all songs gathered. Since no earlier literature on aspects of popular music over a long period of time has been found, all meta data of The Echo Nest was downloaded. An overview of the relevant data which has been used for this thesis, is shown in table 1. When analyzing previous research (chapter 2), several musical eras have been identified. In this research, the sixties, seventies, eighties, nineties, zero's and ten's are analyzed (without assigning songs to genres) in order to gain insight in significant aspects in these eras. The musical aspects can be considered as the dependent variables, whereas the period is the independent variable. In order to insert periods in the dataset, the new variable 'period' needed to be inserted in the dataset. Years 1965 through 1969 were transformed into the value '60', the years 1970 through 1979 into '70', et cetera. In conducting this research, the ranking of the songs in the Dutch Top 40 was not included. Since all songs have had a listing in the Dutch Top 40, it infers that all songs have been popular in a certain time frame.

3.2.1 Pre-tests Answering the question whether musical aspects did change over the period of 1965 to 2014 may be considered as explorative research since no earlier research has been conducted. The first experiment (section 4.1) shows three pre-tests: a visualization of possible changes of musical aspects between periods (sixties, seventies, eighties, nineties, zeros and tens), correlations between musical aspects in the dataset and the normal distribution of musical aspects. These tests provide us insight in developments in musical aspects in popular music and provides insight in our dataset. First, a line chart of the musical aspects ranging from zero to one has been produced. It shows the means of all musical aspects in the respective periods and provided a basis for the comparison of musical aspects over periods, since it yielded possible developments in musical aspects over time. Furthermore, comparable line charts were produced for tempo and loudness, which are measured on a different scale. Tempo is measured in beats per minute, whereas loudness is measured in decibels. Next, correlations between all musical aspects were represented in order to show whether multiple musical aspects are strongly related. Lastly, the normal distribution of our dataset has been analyzed, which yielded that our dataset in fact was normally distributed.

3.2.2 Comparing musical aspects over periods The research design of the second experiment, the comparison of musical aspects, shows multiple dependent variables (i.e. the musical aspects) and one independent variable (i.e. period). The means of every individual musical aspect in a certain period were compared to all other periods. Therefore, ANOVAs needed to be conducted (which is the second experiment in this thesis, see section 4.2). Each musical aspect was analyzed separately and results were produced in tables which are shown in sections 4.2.1 to 4.2.11.

Chapter 4: Results In this chapter, all results are provided. First, a graph which shows differences in musical aspects in our dataset is showed (section 4.1). Furthermore, table 2 shows whether musical aspects are correlated. Next, tests are conducted in order to check whether the dataset is normally distributed. Sections 4.2 through 4.10 show ANOVAs which are conducted for each separate musical aspect, with a post hoc Tukey HSD test to show each significant difference.

4.1 Pre-tests Before conducting ANOVAs, several tests are to be conducted in this research. First, we wanted to visualize whether differences might be expected (section 4.1.1). Second, we want to know whether musical aspects in our dataset are correlated (section 4.2.2) in order to show which musical aspects might influence each other and therefore might measure the same aspect. Third, research needs to check whether the data is normally distributed (section 4.2.3) in order to check whether most of the values lie round a central mean. By doing this, one prevents to draw conclusion based on a non-normal dataset which contains many outliers. A Skewness-Kurtosis test on the normal distribution of the dataset showed no abnormalities, which means that the dataset which has been used in this thesis is normally distributed.

4.1.1 Differences in musical aspects Below, three figures with means of musical aspects within periods are plotted. Figure 1 shows the musical aspects which are measured with values between zero and one. Figure 2 shows tempo, whereas figure 3 provides information on the loudness of hit songs. As figure 1 shows, trends may be considered: first, song hotttnesss increases during the period 1965-2014. Second, since the seventies, artist hotttnesss increases as well. Third, artist familiarity values increase since the seventies and fourth, the energy of a song increases up until the zeros but decreases in the tens. Fifth, the valence of a song decreases since the eighties and lastly, the values of danceability increase from the sixties through the eighties, but stabilizes in the period from the eighties through the tens. Figures 2 and 3 show no major differences in tempo over a year, since the number of beats per minute vary between approximately 115 and 120 on a large scale, whereas the loudness varies between approximately -10.5 and -8 (on a large scale as well). However, differences may be found in the next section.

Figure 1 Mean of musical aspects (ranging from zero to one) per period

Figure 2 Mean tempo (in beats per minute) per period

Figure 3

Mean loudness (in decibels) per period

4.1.2 Correlation Table 2 shows correlations between musical aspects. As said before, every individual aspect was compared to another aspect in order to control whether two measured musical aspects are in fact the same. Table 2 shows some strong correlations: song hotttness and artist hotttnesss (r = .690), song hotttnesss and artist familiarity (r = .677), energy and loudness (r = .677) and energy and acousticness (r = .472). Only one correlation coefficient may be considered as very strong, namely the correlation between artist hotttnesss and artist familiarity (r = .883). Correlations represent values between minus one and one. In this thesis, all strong and very strong correlations are positive (i.e. a value above zero), which means that, for example, a higher value on artist hotttnesss normally represents a higher value on artist familiarity as well (r = .883). Therefore, all strong and very strong correlations imply that these musical aspects influence each other.

Table 2 Correlations between musical aspects Song Artist Artist Energy Liveness Tempo Speechi- Acoustic- Loud- Valence Dance- hotttnesss hotttnesss familiarity ness ness ness ability Song .690** .677** -.030** .012** -.069** .051** -.076** -.002 -.149** .014** hotttnesss .000 .000 .000 .001 .000 .000 .000 .518 .000 .000 Artist .883** -.035** .032** -.067** .046** -.073** .008* -.190** -.055** hotttnesss .000 .000 .000 .000 .000 .000 .029 .000 .000 Artist -.044** .034** -.078** .037** -.059** -.040** -.157** -.060** Familiarity .000 .000 .000 .000 .000 .000 .000 .000 Energy .107** .229** .123** -.472** .677** .316** .182** .000 .000 .000 .000 .000 .000 .000 Liveness .008* .125** .039** .001 -.111** -.261** .018 .000 .000 .773 .000 .000 Tempo .053** -.124** .140** .077** -.069** .000 .000 .000 .000 .000 Speechi- -.056** .022** .046** .128** ness .000 .000 .000 .000 Acoustic- -.253** -.187** -.218** ness .000 .000 .000 Loudness .081** .062** .000 .000 Valence .527** .000 Dance- ability

Note: the *-symbol means that a correlation is significant at the 0.05 level, the **-symbol means that a correlation is significant at the 0.01 level. The upper number shows the intensity of the correlation, the lower number shows the significance.

4.2 Comparing musical aspects over periods Below, the results of all ANOVAs are shown in tables. The first column shows the period which has been tested, with the means of the sixties and standard deviations of the respective periods between brackets. Furthermore, the first period in the first column (i.e. 60) is called I, whereas the second period is called J. The second column shows the mean difference (I-J) between the periods; a negative difference means an increase in the mean of a respective aspect, a positive difference means a decrease in the mean of the respective aspect. Symbols in the second column show insignificant differences in means between certain periods; if no symbol is set, the mean difference is significant at the 0.05 level. The symbols are explained below the respective tables.

4.2.1 Song hotttnesss An ANOVA showed a significant effect of song hotttnesss on the periods at the p <.001 level for the six conditions [F(5, 77483) = 1873.17, p <.001]. Post hoc comparisons using the Tukey HSD test showed significant differences (at the p <.05 level) between all periods (see table 3).

Table 3 Song hotttnesss Tukey HSD test Period (SD) Mean difference (M '60' = 0.26) 60 (0.16) -70 (0.17) 0.01 -80 (0.17) -0.04 -90 (0.17) -0.06 -00 (0.18) -0.11 -10 (0.22) -0.22

Table 3 shows a trend which points to a significant increase in song hotttness during the period 1965-2014, since the mean differences increase negatively over time. The mean difference compared to the sixties increases when more decades have passed by, which means increasing song hotttnesss (i.e. how trending a song is) in hit songs.

4.2.2 Artist hotttnesss An ANOVA showed a significant effect of artist hotttnesss on the periods at the p <.001 level for the six conditions [F(5, 77618) = 2261.25, p <.001]. Post hoc comparisons using the Tukey HSD test showed significant differences (at the p<. 05 level) between all periods (see table 4).

Table 4 Artist hotttnesss Tukey HSD test Period (SD) Mean difference (M '60' = 0.53) 60 (0.15) -70 (0.15) 0.02 -80 (0.14) -0.02 -90 (0.15) -0.03 -00 (0.19) -0.11 -10 (0.19) -0.20

Table 4 points out a trend in which the mean of artist hotttnesss (i.e. how trending an artist is at a particular moment in time) increased the Dutch Top 40, by showing a mean difference which increases in a negative way.

4.2.3 Artist familiarity An ANOVA showed a significant effect of artist familiarity on the periods at the p <.001 level for the six conditions [F(5, 77483) = 567.53, p <.001]. Post hoc comparisons using the Tukey HSD test showed significant differences between most of the periods (see table 5).

Table 5 Artist familiarity Tukey HSD test Period (SD) Mean difference (M '60' = 0.53)

60 (0.21) -70 (0.19) 0.04 -801 (0.18) -0.02 -90 (0.18) -0.021 -002 (0.18) -0.06 -10 (0.17) -0.072 Note: 1Period 80-90 (p = .962) 2Period 00-10 (p = .970)

Table 5 shows a slight trend of increasing artist familiarity (i.e. the chance of asking an individual who knows the performing artist of a song) when scoring a hit in the Dutch Top 40. The trend which can be extracted is not as strong as in sections 4.2.1 and 4.2.2, since the mean differences are not as large as in the previous two sections. However, since most of the periods significantly differ (at the p <.05 level), the development is present.

4.2.4 Energy An ANOVA showed a significant effect of energy on the periods at the p <.001 level for the six conditions [F(5, 77483) = 586.23, p <.001]. Post hoc comparisons using the Tukey HSD test showed significant differences (at the p <.05 level) between most of the periods (see table 6).

Table 6 Energy Tukey HSD test Period (SD) Mean difference (M '60' = 0.57) 60 (0.20) -70 (0.20) -0.05 -801 (0.20) -0.09 -90 (0.22) -0.11 -00 (0.20) -0.12 -10 (0.23) -0.081 Note: 1Period 80-10 (p = .143)

The energy of a song is relatively constant in the period 1965-2014 (table 6). The sixties period shows the lowest amount of energy in a song (M = 0.57, SD = 0.20), whereas the other periods roughly show a comparable amount of energy in hits. It means that, after the sixties, songs became more powerful and intense. 26

4.2.5 Liveness An ANOVA showed a significant effect of liveness on the periods at the p<.001 level for the six conditions [F(5, 77472) = 43.49, p <.001]. Post hoc comparisons using the Tukey HSD test showed significant differences (at the p <.05 level) between most of the periods (see table 7).

Table 7 Liveness Tukey HSD test

Period (SD) Mean difference (M '60 = 0.27) 601 (0.24) -70 (0.24) -0.001 -803 (0.24) 0.02 -902 4 (0.22) 0.03 -005 (0.21) 0.032 -10 (0.21) 0.023 4 5 Note: 1Period 60-70 (p = 1.000) 2 Period 90-00 (p = .991) 3Period 80-10 (p = 1.000) 4Period 90-10 (p = .190) 5Period 00-10 (p = .064)

As table 7 shows, the liveness of a hit song shows no particular trend. Some significant differences were found but the differences were not as large as in previous sections. Therefore, low liveness (a value below 0.6 most likely represents studio recordings) in a hit song in the Dutch Top 40 can be considered as a stable musical aspect.

4.2.6 Speechiness An ANOVA showed a significant effect of speechiness on the periods at the p <.001 level for the six conditions [F(5, 77415) = 353.40, p <.001]. Post hoc comparisons using the Tukey HSD test showed significant differences between most of the periods (see table 8).

Table 8 Speechiness Tukey HSD test 27

Period (SD) Mean difference (M '60' = 0.05) 601 2 (0.06) -703 (0.05) -0.001 -80 (0.04) -0.002 3 -90 (0.06) -0.01 -00 (0.08) -0.02 -10 (0.07) -0.02 Note: 1Period 60-70 (p = .449) 2Period 60-80 (p = .999) 3Period 70-80 (p = .421)

Slight differences in speechiness (i.e. number of spoken words in a recording) were found (table 8). However, since the mean differences nearly do not change during multiple decennia since the sixties, speaking of a trend might be considered as exaggerated.

4.2.7 Acousticness An ANOVA showed a significant effect of acousticness on the periods at the p <.001 level for the six conditions [F(5, 77443) = 956.90, p <.001]. Post hoc comparisons using the Tukey HSD test showed significant differences (at the p <.05 level) between all periods (see table 9).

Table 9 Acousticness Tukey HSD test Period (SD) Mean difference (M '60' = 0.39) 60 (0.29) -70 (0.28) 0.08 -80 (0.24) 0.14 -90 (0.28) 0.19 -00 (0.32) 0.23 -10 (0.30) 0.20

Table 9 shows that hit songs have become less acoustic, compared to the sixties. A trend is noticeable: since the sixties, hit songs in subsequent decennia became less acoustic which means that, for example, more synthesizers and electronic instruments were used.

4.2.8 Valence An ANOVA showed a significant effect of valence on the periods at the p <.001 level for the six conditions [F(5, 77457) = 611.26, p <.001]. Post hoc comparisons using the Tukey HSD test showed significant differences (at the p <.05 level) between most of the periods (see table 10).

Table 10 Valence Tukey HSD test

Period (SD) Mean difference (M '60' = 0.63) 60 (0.23) -70 (0.24) -0.02 -80 (0.24) -0.03 -901 (0.25) 0.06 -00 (0.24) 0.061 -10 (0.24) 0.12 Note: 1Period 90-00 (p = .901)

The valence of a song (table 10) increased in the seventies and eighties but since the nineties, the means of this musical aspect dropped which means that, on average, the positivity of hit songs has dropped since two and a half decennium.

4.2.9 Danceability An ANOVA showed a significant effect of danceability on the periods at the p <.001 level for the six conditions [F(5, 77483) = 1091.60, p <.001]. Post hoc comparisons using the Tukey HSD test showed significant differences (at the p <.05 level) between most of the periods (see table 11).

Table 11 Danceability Tukey HSD test

Period (SD) Mean difference (M '60' = 0.51) 60 (0.15) -70 (0.15) -0.04 -80 (0.15) -0.12 -901 (0.15) -0.10 -00 (0.15) -0.11 -10 (0.13) -0.101 Note: 1Period 90-10 (p = .051)

As table 11 shows, danceability increased since the sixties. However, since the eighties, no trend can be identified. Since the seventies, hit songs in the Dutch Top 40 can be considered as moderate when looking at energy. This means that tempo, rhythm stability, beat strength and overall regularity, on average, stay consistent in hit songs in the Dutch Top 40.

4.2.10 Tempo An ANOVA showed a significant effect of tempo on the periods at the p <.001 level for the six conditions [F(5, 77483) = 69.50, p <.001]. Post hoc comparisons using the Tukey HSD test showed significant differences (at the p <.05 level) between most of the periods (see table 12).

Table 12 Tempo (in beats per minute) Tukey HSD test

Period (SD) Mean difference (M '60'= 115.58) 60 (25.74) -701 3 (26.34) -4.66 -80 (23.26) -2.22 -902 4 (26.68) -5.51 -005 (25.28) -4.791 2 -10 (24.26) -4.36 3 4 5 Note: 1Period 70-00 (p = .997) 2Period 90-00 (p = .102) 3Period 70-10 (p = .981) 4Period 90-10 (p = .063) 5Period 00-10 (p = .903)

Tempo increased in all periods when compared to the sixties. The eighties showed a slight decrease in table 12, but the means of this musical aspect was higher in every decade since the sixties. However, a large number of differences was insignificant, which means that the tempo in hit songs in the Dutch Top 40 stayed consistent (on average).

4.2.11 Loudness An ANOVA showed a significant effect of loudness on the periods at the p <.001 level for the six conditions [F(5, 77483) = 661.80, p <.001]. Post hoc comparisons using the Tukey HSD test showed significant differences (at the p <.05 level) between most of the periods (see table 13).

Table 13 Loudness (in decibels) Tukey HSD test

Period (SD) Mean difference (M '60' = -10.31) 601 (3.96) -70 (3.56) -0.22 -80 (4.18) -0.081 -90 (4.08) -0.81 -00 (4.35) -2.12 -10 (4.66) -1.90 Note: 1Period 60-80 (p = .734)

The mean of loudness increased over time, compared to the sixties (table 13), which means that, overall, hit songs in the Dutch Top 40 became louder over time.

Chapter 5: Discussion and conclusion The last chapter of this thesis is divided into several sections. First, the method, results and discussion of the pre-tests (section 5.1) and the comparison of musical aspects over periods (section 5.2) are being dealt with. Next, limitations of this research are mentioned, as is future work (section 5.3).

5.1 Pre-experiment tests Experiment one dealt with several tests regarding the dataset which has been used. First, some differences in musical were plotted in a graph, in which mean differences per period were visualized. Figure 1 showed that song hotttnesss and artist hotttnesss increased in the period 1965-2014, which means that, in hit songs in the Dutch Top 40, 'how trending a song or artist is at a particular moment in time' increased over time. It may yield that, in general, an artist's popularity contributes to a quotation in the Dutch Top 40, as well as the popularity of a song. The latter sounds rather obvious. However, why does hotttnesss increase over time? A possible explanation for this phenomenon is the internet, where popularity of songs can be measured via, for example, number of views on YouTube or the rated popularity of songs on MySpace. The latter measure was used by Berns, Capra, Moore and Noussair (2010) and these researchers showed that song popularity had a significant, positive effect on their participants' rating of songs. The same effect may occur when measuring the popularity of an artist. Furthermore, figure 1 showed that the artist familiarity in hit songs in the Dutch Top 40 increased since the seventies, which may be contributed to increasing media attention via television, radio and the internet. A third phenomenon which was showed by figure 1 is that the energy of a song increased until the zeros, but dropped in the tens. No explanation for this trend can be given at this moment, as is for the phenomena which were showed in valence (decreases since the eighties) and danceability (increases from the sixties to the eighties and remains stable ever since). It means that it, at this moment, remains unclear why particular values regarding a song's energy, positivity or suitability to dance to it, occur in hit songs in the Dutch Top 40. Figures 2 and 3 show no major differences in tempo over a year, since the number of beats per minute vary between approximately 115 and 120 on a large scale, whereas the loudness varies between approximately -10.5 and -8 (on a large scale as well).

The second pre-test showed correlations between musical aspects and these correlations were listed in table 2. The results showed some moderate correlations between song hotttnesss and artist hotttnesss (r = .690), song hotttnesss and artist familiarity (r = .677), energy and loudness (r = .677) and energy and acousticness (r = .472). One correlation could be considered as strong, namely the correlation between artist hotttnesss and artist familiarity (r = .883). However, figure 2 showed that most correlations between musical aspects (apart from the previously mentioned correlations) are weak, so that our ANOVA-results could withstand criticism on correlations. Third, the normal distribution of our dataset was tested. A Skewness-Kurtosis test on the normal distribution of the dataset showed no abnormalities, which means that the dataset which has been used in this thesis is normally distributed. This test strengthens the conclusions of this thesis.

5.2 Comparing musical aspects over periods Experiment two concerned the ANOVA-tests in which we compared means of each musical aspect (dependent variables) in different periods of time (independent variables). In this research, periods were divided into the sixties, seventies, eighties, nineties, zeros and tens, since these periods are used informally in order to classify musical eras. A number of results have been found in this research. First, song hotttnesss increased during the period between the sixties and the tens, with a growing significant difference between means. Second, artist hotttnesss increased during the eras, since the mean difference in the periods between the sixties and tens became larger as well. A possible explanation has been given in section 5.1, namely the introduction of the internet as reference for music listeners for popularity of an artist's or song's popularity. This popularity (or hotttnesss) is measured via, for instance, number of views on YouTube or rated popularity on MySpace. Furthermore, figure 1 showed a more or less parallel development in both song and artist hotttnesss, which might yield that these musical aspects are strongly correlated, which was the case as could be seen in table 2 (r = .690). Third, a trend was spotted regarding artist familiarity, which showed a slightly increasing mean of artist familiarity on scoring a hit in the Dutch Top 40. The latter trend can be considered as striking, since artist familiarity and artist hotttnesss were strongly positively correlated (r = .883). Artist familiarity may increase due to media attention as well, which explains the strong correlation between both variables. Furthermore, artist familiarity and

34 song hotttnesss were moderately correlated as well (r = .677), which -again- may be explained by increasing media attention. Next, the amount of energy in a song remained relatively constant during the period 1965-2014. The amount of energy of latter periods, when compared to the sixties, was slightly higher but remained constant over the years. The chapter on background literature (chapter 2) already showed that each period had one or more genres which may be considered as energetic: Motown/R&B and hard rock (sixties) disco and funk/soul (seventies), hair metal (eighties) and pop music (eighties, nineties and twenty-first century) and gabber (nineties). The presence of energetic genres in each period may explain the relatively constant means of energy in hit songs in the Dutch Top 40 and could be expected when analyzing chapter 2. The presence of several genres in each period which might be regarded as danceable might explain the relatively constant mean of danceability (M = 0.51) as well. The Dutch Top 40 in the sixties (the British invasion, Motown/R&B), seventies (disco, funk/soul), eighties (pop music) nineties (gabber and pop music) and the twenty-first century (pop music) could all show danceable genres. The amount of liveness (M = 0.25) and speechiness in music showed no particular trend as well, which means that, on average, studio recordings and songs with a low degree of spoken text form the majority of songs in the Dutch Top 40 in all periods. Since no major differences have been found, the average liveness and speechiness remained relatively constant during the period 1965-2014 in the Dutch Top 40. However, no justification for these results has been found. Furthermore, tempo (M = 115.58) showed most insignificant differences, compared to the other musical aspects. Especially since the zeros, insignificant differences were found. Therefore, no development in tempo in the twenty-first century could be found. However, in the twentieth century, differences were found which show, on average, an increase in tempo over time. Genres like disco/club and punk rock/new wave (seventies), hair metal (eighties) and gabber (nineties) might have contributed to this increase. Next, the mean acousticness of a song decreased since the sixties. This development could be expected, since synthesizers (for example in the eighties), altered voices, computer technologies and amplified instruments are considered to play a more and more important role since the sixties. Furthermore, the valence of a song increased in the seventies, but the mean declined since the eighties and lastly, songs became louder in the period 1965-2014. Especially since

35 the zeros, the loudness of a hit song increased in decibels. These trends are striking but no explanation could be given. As can be seen above, five of the eleven investigated musical aspects (energy, danceability, liveness, speechiness and tempo) remained relatively constant in the period 1965-2014, which means that the means of these aspects can be considered as relatively robust when scoring a hit in the Dutch Top 40.

5.3 Limitations and future work As this research can be considered as explorative, some limitations can be defined. First, in this research, we informally defined musical periods as the sixties, seventies, eighties, nineties and tens. However, other results may be found when investigating shorter periods of time (e.g. five years), since musical eras may be shorter than ten years. As we have seen in sections 2.1.1 to 2.1.5, several genres may be dominant in a period of ten years. Therefore, the musical aspects regarding the genres within a period of ten years may differ. The genres of hit songs in the Dutch Top 40 were not a topic of this research. However, future work may specialize itself on the genres within a dataset in order to define musical aspects of hit songs within a genre. Furthermore, future research may deal with clustering of musical aspects per genre as well. In this research, each musical aspects has been analyzed separately. However, one may cluster musical aspects in order to predict an outcome in genre. Furthermore, especially during the beginning years of the Dutch Top 40, the dataset shows Dutch songs such as 'iedere avond' (by Ronnie Tober), 'welterusten mijnheer de president' (by Boudewijn de Groot) and 'je bent niet hip' (by Patricia Paay). These Dutch hit songs did not become hits in other countries; therefore, a dataset such the UK Top 40 Singles charts (BBC, n.d.) or the USA Singles Top 40 (At 40, n.d.) may yield other results. Next, the method in this research did not assign ranking to hit songs, but may be included in future work, since hit songs which had a higher rating in the Dutch Top 40 (e.g. top ten) may differ in musical aspects from hit songs which had a ranking between 30 and 40. Lastly, the dataset showed a large amount of significant differences in musical aspects over time, which can be considered as striking. In this thesis, no justification for these results was found. However, some possible explanations for differences in popular music might be derived from section 2.2, in which developments in popular music are discussed. Shuker (2001) mentioned that a few large music companies dominate the market of popular music (for example Sony Music Entertainment, Warner Music Group and Universal/Polygram) and

36 that capitalism is the main motive for producing popular music. Since only a small fraction of the offered products may prove to be profitable (Hendricks & Sorensen, 2009), it may be expected that popular music is adjusted to the wishes of the mass public. Therefore, developments in several musical aspects in the Dutch Top 40 which have been showed in this thesis, may be attributed to developing wishes of the mass public. Shuker (2001) named the interaction between public and companies as well, as a middling factor between corporate and consumer power. As can be seen, this thesis has its limitations. However, it contains a research method which can be used for future research on musical aspects in periods of time based on other, comparable datasets which are downloaded and inserted in The Echo Nest or comparable web services. Furthermore, it provides multiple possibilities for future research on musical aspects.

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