Genetic Algorithms at Shazam
I spent this last summer working at Shazam as part of their R&D team in Redwood City. I wrote a blog post detailing the project I worked on here.
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Classifying Music Genres via Lyrics using a Hierarchical Attention Network
As part of CS224N here at Stanford I began learning about the various uses of deep learning in natural language processing. As part of this course, I decided to begin a project to try and classify music genre using lyrics only which has typically been a tough problem in the music information retrieval (MIR) field. At the culmination of the course I was so invested in the course that I continued working on it and eventually published this research in ISMIR 2017, held in Suzhou, China.
To begin the project I took inspiration from the paper by Yang et al. using a Hierachical Attention Network (HAN) to classify documents. Similarly to documents, lyrics contain a hierachical structure: words go into lines, lines into sections (verse/chorus/…), and sections then form the whole song. Further, from the attention mechanism we can then extract and visualise where the network is applying its weights.