Matching Items (2)
Filtering by
- All Subjects: Spotify
- Creators: DiMuro, Louis
- Creators: Haas, Kyle
Description
Music is an integral part of a community's identity, and music streaming has changed the way in which people interact with popular music as a whole. While significant research has been done regarding how streaming services have impacted the way users engage with music, little has been done to account for how streaming has changed the creation of new music. Additionally, globalization in music results in unique hybrid genres rather than complete adoption of global culture, making it hard to measure the global impact on regional sounds, as chart diversity alone cannot account for this unique interaction. This research addresses this gap in literature by utilizing Spotify’s audio features to analyze regional popular music characteristics from 2010 through 2020 using the Top 100 tracks from the global, Korean, and Japanese charts. It then observes whether the chart data demonstrates a convergence or divergence in relation to the musical attributes of global popular music and the growth of music streaming, and if it is reflecting a globalization effect. The results suggest that local artists reflect global trends in already globalized markets, and that streaming may be having a heterogenization effect on popular music. Additionally, the data also suggests that observing the musical characteristics of a region may be able to measure how globalized a region's music culture is, allowing for the observation of globalization beyond looking at chart diversity and instead observing the music characteristics of domestic artists.
ContributorsHaas, Kyle (Author) / Proferes, Nicholas (Thesis advisor) / Halavais, Alexander (Committee member) / Walker, Shawn (Committee member) / Arizona State University (Publisher)
Created2022
Description
Although Spotify’s extensive library of songs are often seen broken up by “Top 100” and main lyrical genres, these categories are primarily based on popularity, artist and general mood alone. If a user wanted to create a playlist based on specific or situationally specific qualifiers from their own downloaded library, he/she would have to hand pick songs that fit the mold and create a new playlist. This is a time consuming process that may not produce the most efficient result due to human error. The objective of this project, therefore, was to develop an application to streamline this process, optimize efficiency, and fill this user need.
Song Sift is an application built using Angular that allows users to filter and sort their song library to create specific playlists using the Spotify Web API. Utilizing the audio feature data that Spotify attaches to every song in their library, users can filter their downloaded Spotify songs based on four main attributes: (1) energy (how energetic a song sounds), (2) danceability (how danceable a song is), (3) valence (how happy a song sounds), and (4) loudness (average volume of a song). Once the user has created a playlist that fits their desired genre, he/she can easily export it to their Spotify account with the click of a button.
Song Sift is an application built using Angular that allows users to filter and sort their song library to create specific playlists using the Spotify Web API. Utilizing the audio feature data that Spotify attaches to every song in their library, users can filter their downloaded Spotify songs based on four main attributes: (1) energy (how energetic a song sounds), (2) danceability (how danceable a song is), (3) valence (how happy a song sounds), and (4) loudness (average volume of a song). Once the user has created a playlist that fits their desired genre, he/she can easily export it to their Spotify account with the click of a button.
ContributorsDiMuro, Louis (Author) / Balasooriya, Janaka (Thesis director) / Chen, Yinong (Committee member) / Arts, Media and Engineering Sch T (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05