Topic: Using Spotify data to predict what songs will be hits
+Cook My Own Breakfast !!IpftyHSqdT — 6.7 years ago #54,011
Two students and researchers at the University of San Francisco (USF) have recently tried to predict billboard hits using machine-learning models. In their study, pre-published on arXiv, they trained four models on song-related data extracted using the Spotify Web API, and then evaluated their performance in predicting what songs would become hits.
"I'm a huge music fan, and I listen to music all day; during my commute, at work, and with friends," Kai Middlebrook, one of the researchers who carried out the study, told TechXplore. "Last spring, I began a research project on automatic music genre classification with professor David Guy Brizan at the University of San Francisco (USF). The project required a large amount of music data, and popular music streaming services have exactly the kind of data I needed."
While he was working on a project related to automatic music genre classification, Middlebrook learned that Spotify allows developers to access its music data. This encouraged him to start experimenting with the Spotify Web API to collect data for his studies. Once he completed the research related to genre classification, however, he set the API aside for some time.
"A few months later, my friend Kian, who is also a data scientist and loves music, and I had a discussion about music," Middlebrook said. "At some point during the conversation, the generally held idea that "all hit songs sound the same" was brought up. We didn't necessarily believe that it was true, but the idea made us wonder: What if hit songs do share some similarities? It seemed possible, so Kian and I decided to investigate further."
Middlebrook and Sheik, who had previously collaborated on the genre classification project, decided to carry out a further investigation using data extracted from Spotify. This new project would also be the final assignment for their data mining course at USF.
"We were collaborating on several other projects for various courses, so it made sense to stick together," Kian Sheik, another researcher involved in the study, told TechXplore. "Lil Nas X's hit "Old Town Road" had just come out of nowhere, and was on the top of the Billboard Hot 100. Kai and I wondered if a computer could have predicted his rise, or if it was just a hit single that came out of left field. What started as a simple final project ended with us exhausting all of state-of-the-art supervised learning models on a large dataset to answer a simple question: Will this song be a hit?"
Full news item: https://techxplore.com/news/2019-09-spotify-songs.html
"I'm a huge music fan, and I listen to music all day; during my commute, at work, and with friends," Kai Middlebrook, one of the researchers who carried out the study, told TechXplore. "Last spring, I began a research project on automatic music genre classification with professor David Guy Brizan at the University of San Francisco (USF). The project required a large amount of music data, and popular music streaming services have exactly the kind of data I needed."
While he was working on a project related to automatic music genre classification, Middlebrook learned that Spotify allows developers to access its music data. This encouraged him to start experimenting with the Spotify Web API to collect data for his studies. Once he completed the research related to genre classification, however, he set the API aside for some time.
"A few months later, my friend Kian, who is also a data scientist and loves music, and I had a discussion about music," Middlebrook said. "At some point during the conversation, the generally held idea that "all hit songs sound the same" was brought up. We didn't necessarily believe that it was true, but the idea made us wonder: What if hit songs do share some similarities? It seemed possible, so Kian and I decided to investigate further."
Middlebrook and Sheik, who had previously collaborated on the genre classification project, decided to carry out a further investigation using data extracted from Spotify. This new project would also be the final assignment for their data mining course at USF.
"We were collaborating on several other projects for various courses, so it made sense to stick together," Kian Sheik, another researcher involved in the study, told TechXplore. "Lil Nas X's hit "Old Town Road" had just come out of nowhere, and was on the top of the Billboard Hot 100. Kai and I wondered if a computer could have predicted his rise, or if it was just a hit single that came out of left field. What started as a simple final project ended with us exhausting all of state-of-the-art supervised learning models on a large dataset to answer a simple question: Will this song be a hit?"
Full news item: https://techxplore.com/news/2019-09-spotify-songs.html
+Dr. Chattanooga !!SlFawOoKvd — 6.7 years ago, 26 minutes later[T] [B] #571,288
fagbuysmusic.jpg+Anonymous C — 6.7 years ago, 1 hour later, 1 hour after the original post[T] [B] #571,295
Where's fake Merrin? Oh wait, it's @OP!
·Cook My Own Breakfast !!IpftyHSqdT (OP) — 6.7 years ago, 4 hours later, 6 hours after the original post[T] [B] #571,318
+Anonymous D — 6.7 years ago, 1 hour later, 7 hours after the original post[T] [B] #571,340
·Anonymous C — 6.7 years ago, 2 hours later, 10 hours after the original post[T] [B] #571,363
·Cook My Own Breakfast !!IpftyHSqdT (OP) — 6.7 years ago, 1 week later, 1 week after the original post[T] [B] #572,574
(Citing a deleted or non-existent reply.)
no
no
+Anonymous E — 6.7 years ago, 1 day later, 1 week after the original post[T] [B] #572,618
@OP
i say FUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUCK YOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOUUUUUU to:
SPOTIFY
YOUTUBE
SNAPCHAT
INSTAGRAM
FACEBOOK
WIKIPEDIA
TWITTER
REDDIT
DISCORD
GOOGLE
TWITCH
ALL THE SHITTY NEWS SHIT SITES
TUMBLR
NETFLIX
SOUNDCLOUD
4CHAN
SHIT LIKE BUZFED AND CRAKED
bitch !
i say FUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUCK YOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOUUUUUU to:
SPOTIFY
YOUTUBE
SNAPCHAT
WIKIPEDIA
DISCORD
TWITCH
ALL THE SHITTY NEWS SHIT SITES
TUMBLR
NETFLIX
SOUNDCLOUD
4CHAN
SHIT LIKE BUZFED AND CRAKED
bitch !
+Anonymous F — 6.7 years ago, 10 minutes later, 1 week after the original post[T] [B] #572,619
+Anonymous G — 6.7 years ago, 1 hour later, 1 week after the original post[T] [B] #572,620
I'll crack your ass harder than a fat cracker cracks his crackers.
·Cook My Own Breakfast !!IpftyHSqdT (OP) — 6.7 years ago, 1 day later, 2 weeks after the original post[T] [B] #572,772
LOL
Start a new topic to continue this conversation.
Or browse the latest topics.