Image created by Valeria Morales with the elements taken from "The Noun Project." Jukebox by André Luiz, mp3 by Dinosoft Labs, music by yudi, headphones by Three Six Five, record by Car Badrun, internet by ProSymbols, computer by Iga, cursor by iejank, and music by Ali Riza Saçan, all from the Noun Project.
INTRODUCTION
We chose to use the data set “the Evolution of Popular Music: 1960-2010” made by Matthias Mauch, Robert M. MacCallum, Mark Levy, and Armand M. Leroi who took the Billboard Hot 100 list from every quarter from 1960 to 2010 to investigate the evolution of popular music taste. We are researching how the emergence of the internet and different technologies impacted the diversity of popular songs on the Billboard Hot 100. Our prediction is that as the Internet and other technologies became more prominent, the genres represented in the Billboard Hot 100 became more diverse. This is because we believe the internet lowered the barrier for different artists to enter the music industry as well as increased communication between diverse groups of people. Many articles in mainstream media and academia use the Billboard Hot 100 as the main determinant of popular music over time. This has been ascribed to the chart’s ability to adapt to a variety of trends in the music industry, as NPR wrote, “[it was] built to absorb whatever medium is delivering music to the masses at any given time.” Because the Internet was invented in August 6, 1991, we decided to narrow down the expansive dataset to only 1980 to 2010. The dataset mostly focuses on the results from statistical techniques like k-means clustering and principal component analysis, based off harmonic and timbre scores. While it helps quantify aspects of the music, it removes the emotional aspect of the music which would warrant the harmonic and timber choices in these top songs in the first place. This dataset can also potentially reveal the evolution of certain genres of music. Using the measure of chord changes for example we could potentially analyze the level of sophistication or simplicity of certain of music overtime. By using the year the songs were released, we could analyze the ways in which the emergence of the internet has impacted genre diversity.
Due to the ubiquity and vastness of our data, we had to draw from many different sources of literature to create a wholesome and comprehensive understanding of our topic, however this caused us to have a very diverse collection of articles and books that don’t share one belief or opinion. Consequently, we have many authors that agree and many authors that disagree on a plethora of ideas. Gerard and Pinch both acknowledge and agree upon the disruptive nature of technological change and how it has led to a vast increase in diversity of songs and artists. However, they disagree on the attributes that caused this change, Gerard believes that a lack of copyright protection with new means of distributing music has lead to a vast increase in diversity while Pinch argues that it’s because new synthesizers are allowing artists to experiment with millions of more sounds to create music. While some papers claim that musical aspects like timbre are useful for quantifying and classifying music (the main author of our dataset specifically uses timbre in PCA and k-means clustering), others like Aucouturier argue the opposite. Articles about music startups in 2011 and the lyrical trends about Top 40 song charts during the same time period have added to our understanding of the music market and its distributed nature, as well as how genre popularity will have shifted over time. Unfortunately, while our bibliography has an exuberant amount of information, none of them specifically address a data set of music that they have gathered over the years, just generalizations. This is where we come in since we have all the information and opinions of our articles and we have a data set of popular music over the last few decades. Now we just have to combined the two.
This project is important because it analyzes the relationship between technological milestones and generational music tastes. A lot of the literature we looked at that analyzed this connection were a bit outdated and our project seeks to understand how non-capital methods of listening to music like streaming have impacted cultural taste in music. This project is also different because it is using a data set and cross analyzing it with technological milestones that a lot of earlier articles did not account for. The project is also exploring the specific relationship between musicians and the internet. The internet has transitioned over time leading to the creation of social media, music applications such as Spotify, Soundcloud and so on. It is important to analyze the specific relationship between artists with such. For instance, musicians aimed to form a strong relationship with audiences which is mainly possible by social media outreach like twitter, Instagram and facebook. Artists who were active on such sources were able to relate to audiences more which helped explain why they appeared at a certain position on the charts. On the other hand, artists not utilizing such devices would not be able to form that connection with audiences thus explaining their absence from the chart. External factors being technological devices and the internet may have been key factors in causing their styles to change. The more successful ones adapted to the external factors more appropriately and thus stayed in the limelight. Furthermore, this combined with the use of music applications would help understand the changes in diversity of music overtime. Exploring the lyrical themes of songs also accounts for the diversity and transition of songs overtime. The articles explored specifically highlighted certain features, some of which stayed the same while others changed before and after the development of streaming. We see how references to lifestyle were common in songs but explicit references to drugs and sexual references appeared more in the latter half. This helps understand changes in cultural norms.
The larger question at stake in this project is to understand the relationship between cultural capital and technology. The early billboard charts were based off monetized listens, such as album purchases and radio acquisitions. As a result the Billboard Hot 100 was only sampling groups that could afford the music. The advent of streaming services, and radio opens the door to analyze more people who may not have been able to afford to listen prior to this. Another larger question is to understand how our music is a reflection of cultural norms and values. This project can ask questions about why our favorite songs at the moment are pop and understand that it is tied to a larger system of cultural values. Additionally the development of music technology is codependent as a music style can lead the creation of new technology and vice versa.
Due to the ubiquity and vastness of our data, we had to draw from many different sources of literature to create a wholesome and comprehensive understanding of our topic, however this caused us to have a very diverse collection of articles and books that don’t share one belief or opinion. Consequently, we have many authors that agree and many authors that disagree on a plethora of ideas. Gerard and Pinch both acknowledge and agree upon the disruptive nature of technological change and how it has led to a vast increase in diversity of songs and artists. However, they disagree on the attributes that caused this change, Gerard believes that a lack of copyright protection with new means of distributing music has lead to a vast increase in diversity while Pinch argues that it’s because new synthesizers are allowing artists to experiment with millions of more sounds to create music. While some papers claim that musical aspects like timbre are useful for quantifying and classifying music (the main author of our dataset specifically uses timbre in PCA and k-means clustering), others like Aucouturier argue the opposite. Articles about music startups in 2011 and the lyrical trends about Top 40 song charts during the same time period have added to our understanding of the music market and its distributed nature, as well as how genre popularity will have shifted over time. Unfortunately, while our bibliography has an exuberant amount of information, none of them specifically address a data set of music that they have gathered over the years, just generalizations. This is where we come in since we have all the information and opinions of our articles and we have a data set of popular music over the last few decades. Now we just have to combined the two.
This project is important because it analyzes the relationship between technological milestones and generational music tastes. A lot of the literature we looked at that analyzed this connection were a bit outdated and our project seeks to understand how non-capital methods of listening to music like streaming have impacted cultural taste in music. This project is also different because it is using a data set and cross analyzing it with technological milestones that a lot of earlier articles did not account for. The project is also exploring the specific relationship between musicians and the internet. The internet has transitioned over time leading to the creation of social media, music applications such as Spotify, Soundcloud and so on. It is important to analyze the specific relationship between artists with such. For instance, musicians aimed to form a strong relationship with audiences which is mainly possible by social media outreach like twitter, Instagram and facebook. Artists who were active on such sources were able to relate to audiences more which helped explain why they appeared at a certain position on the charts. On the other hand, artists not utilizing such devices would not be able to form that connection with audiences thus explaining their absence from the chart. External factors being technological devices and the internet may have been key factors in causing their styles to change. The more successful ones adapted to the external factors more appropriately and thus stayed in the limelight. Furthermore, this combined with the use of music applications would help understand the changes in diversity of music overtime. Exploring the lyrical themes of songs also accounts for the diversity and transition of songs overtime. The articles explored specifically highlighted certain features, some of which stayed the same while others changed before and after the development of streaming. We see how references to lifestyle were common in songs but explicit references to drugs and sexual references appeared more in the latter half. This helps understand changes in cultural norms.
The larger question at stake in this project is to understand the relationship between cultural capital and technology. The early billboard charts were based off monetized listens, such as album purchases and radio acquisitions. As a result the Billboard Hot 100 was only sampling groups that could afford the music. The advent of streaming services, and radio opens the door to analyze more people who may not have been able to afford to listen prior to this. Another larger question is to understand how our music is a reflection of cultural norms and values. This project can ask questions about why our favorite songs at the moment are pop and understand that it is tied to a larger system of cultural values. Additionally the development of music technology is codependent as a music style can lead the creation of new technology and vice versa.
This timeline represents both the evolution of the charts they publish and the evolution of the metrics use to calculate the rankings.