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Made at Metis: Two Music Lovers Build Neural Networks

By Emily Wilson • November 20, 2017

This post features two final projects created by recent graduates of our data science bootcamp. Take a look at what's possible in just 12 weeks.

Susan Fung, Ph.D.
Metis Graduate

When recent graduate Susan Fung says that Justin Bieber and Neil Young helped her learn about neural networks, she means it. She wanted to explore neural networks for her final bootcamp project, and as a music fan/neuroscientist, she thought to herself, what better way to do so than by training a neural network using Justin Bieber and Neil Young lyrics? 

"As a neuroscientist, it was too good an opportunity to pass up," she wrote. "What I wanted to do was generate new lyrics based on an artist's style. So at the highest level, train a neural network (NN) on a corpus of Artist X's songs, plug in a seed phrase, receive new text." 

Curious how she got the job done? Read about the entire process on her blog here. She writes about the tools and methods she used throughout, the technical process, and the results, including notes about future work. 
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Matt Murray 

Metis Graduate 

For his final project, graduate Matt Murray created a song recommendation engine with deep learning using Juno Download, a digital download website predominantly used by DJs that has a huge back catalog of tracks for sale. On his blog, Matt writes, "It’s a great music resource and they provide a generous 2-minute sample MP3 file for each song they have for sale. The only problem is…it’s really hard to find music on the site that isn’t a new release or currently top of the sales charts."

While it makes sense that newer music generates the most revenue, Murray's curiosity led him to wonder about the other 99% of musical content on the site. It was hard to find – so how could it be made easier? He felt that the site was missing a content-based "you might also like"-type recommender.

So he started downloading songs and converting them into spectrograms. After that, he trained a CNN (convolutional neural network) on the image data. "I needed to teach it to recognize what the different types of music ‘looked’ like in the spectrogram images, so I used the genre labels and trained it to identify the music genre from the images," he wrote. 

And that's just the start. Visit his blog to read a detailed post on his project. 
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