Katie Claire Ferrier, Historical Events and Average BPM of Songs

I. Introduction

Music has been a continuous cycle of trends in genres, or topics throughout history. Songs often reflect the social changes that are happening during the time period for example during the women’s empowerment movement, music reflected the inequality that women were most frustrated with. This study however is taking a rather numerical approach to music by looking at patterns in beats per minute (BPM) of songs. Usually, songs with a slower BPM are aligned with slower, sad songs while songs with a faster BPM are associated with happy, energetic songs. The methodology will consist of measuring the BPM of the 25 most popular songs of the chosen year and finding the average BPM. This study will find whether this categorizes as a slow, medium, or fast BPM. Once this is collected the major social changes of the year will be considered and proposed as correlations that could explain the reason behind the BPM. The hypothesis is that the year 2020 will have the lowest BPM because of the global pandemic that occurred that year.

II. Literature Review

Correlations in BPM can be seen in numerous other studies, for example an article by Kim Meeri. This article analyzes how music has an influence on mood. Kim discusses how a neuroscientist developed a mathematical formula to investigate why certain songs make us feel “fuzzy” inside. Meeri Kim works as a science writer who contributes regularly to The

Washington Post, Philly Voice, and Oncology Times. She has a Ph.D. and is a member of the National Association of Science Writers and Mentorship Director of the Asian American Journalists Association’s Philadelphia chapter. This article will help connect the correlation between BPM of a song and how this song makes us feel. This will help me better understand the correlation between historical events and the most popular music (Kim).

Another example can be found in “Effects of Sad and Happy Music on Mind-Wandering and the Default Mode Network” by Lilia Taruffi and Corinnia Pehrs.Taruffi and Pehrs argue how music has an effect on our emotional experiences such as happiness and sadness. They found that when listening to sad vs. happy music, people withdraw their attention inwards and engage in spontaneous, self-referential cognitive processes. Taruffi is a lecturer in Music Psychology and Musicology at Durham University. Pehrs is an experienced Postdoctoral Researcher with a PhD in Cognitive Neuroscience. This article will prove relevant to my study because it looks at the science behind our brains reacting to music and how that affects our self-referential cognitive process (Taruffi).

Lastly, author Ying Liu covers the basic information behind how a song is categorized based on the BPM in Effects of Musical Tempo on Musicians’ and Non-Musicians. For example, it defines fast tempo, moderate tempo, and slow tempo in terms of numbers. The editor, Laura Verga, is a postdoctoral researcher at the Max Planck Institute for Psycholinguistics. This article categorizes songs in the three categories listed above based on a numerical BPM, so this will help me when I’m finding the average BPM to categorize them (Liu et al.). III. Method

The method of my research will be a quantitative correlational analysis that first collects the top 20 songs from years 2000, 2005, 2010, 2015 and 2020. After finding the average BPM of the most popular songs of each year, I will use a correlational analysis to find a possible correlation between the fast, moderate, or slow tempo songs along with the events that happened in that year in search of a pattern. This method was developed with a few previous research methods in mind.

In order to collect the most accurate data, this study will be using Billboard Music Charts for the years 2000, 2005, 2010, and 2020. The top 100 songs of each year will be displayed on this database, but this study will only be looking at the

first 20 which will provide an accurate sample size. Each song’s BPM will be recorded and collected, then the 20 BPMs for each year will be added then divided by 20 to essentially find the average. Then in the analysis section of this paper, specific memorable events of each year will be analyzed and correlated to the slow, medium, or fast BPM to reflect trends in emotion during that year.

As stated previously, author Ying Lui defined music into 3 categories. Lui said “fast tempo (>120 bpm), medium tempo (76–120 bpm), and slow tempo (60–76 bpm)”. This study will use the same terms to categorize the average BPM of each year.

One example is author Cecilie Møller who wrote “Beat perception in polyrhythms: Time is structured in binary units”. This source is about how there are patterns in bpm because there are patterns in the way we perceive the emotions of different beats. This is very similar to the research presented here because this study will also be connecting music to emotion, but the gap enlies in the idea that this research will also be connecting the emotion to historical events. Author Cecilie Møller is a researcher at the Aarhus Department of Clinical Medicine and specializes in the Center for Music in the Brain. This source will help develop my method of research by providing a backbone as to how to correlate emotions to music (Møller).

IV. Analysis

This analysis will first start with the year 2000. After completing the method listed above, the average BPM was 114 BPM which falls into the category of medium tempo. This was the slowest tempos of all the years studied, which did not match my hypothesis that 2020 would have the slowest tempos because of the COVID-19 pandemic. In 2005, the average tempo was 116 which still falls into the category of medium tempo, but is slightly higher. In 2010 the average BPM was 118 which is a steady increase. In 2015 the average tempo was 121 which begins to stretch into the fast tempo category, and then in 2020 it goes back down to 116 which shows a decrease. This can be seen in the appendix section (Figure 1).

Upon analysis, there is not a big enough difference between average BPM and year that connecting the music to historical events would prove inadequate. However, it is important to realize that from the year 2000-2020 we experienced major events like 9/11, the Great Recession, and a global pandemic. While this study will not be able to look at the ways those events impacted music, that is a gap that would be beneficial for other researchers to attend. It is also important to notice that through all of these events, Americans stayed constant with medium to high tempo, upbeat songs which is a conclusion within itself.

A key point to this analysis is to recognize the patterns that do appear. Due to the fact that the BPM never fluxated much, we can conclude that over 20 years

pop music remained relatively the same when looking at numerics. Arguably more interesting is that even though the topics were diverse, the BPM’s stayed relatively the same which represents a sole pattern of this study.

This analysis is easy to understand upon looking at Figure 2 in the appendix as the line chart does not fluctuate.

V. Conclusion

In conclusion, while many events from 2000-2020 have changed the way we think or live, the beats per minute of music has held steady for the past 20 years. Upon further research, there has been a study conducted by Jacob Jolij, an assistant professor in cognitive psychology and neuroscience at the University of Groningen that appears to back up and conclude the data collected in this study. Jolij aimed to find a mathematical formula to understand what makes a “feel good song”. He created the FGI (feel good index) that the average feel good pop song had a BPM of 116 (Jolij). This explains the reason why the BPM’s do not fluctuate and furthers the claim that people often listen to music to feel enlightened or happy.

If this study were to be expanded, I would look at the average BPM of popular songs from 1980-2020. This would either further prove or disprove my claim that numerically, music has stayed relatively steady in regards to BPM. While my hypothesis was disproved, this study still represents a contribution to the overall body of knowledge by providing data that BPM exhibits uninterruptible characteristics when compared to the range of history it has been placed in.

*Works Cited page and Figures available upon request.