The 3rd lab from Scalable Machine Learning with Spark has you predict the year a song was published based on features from the Million Song Dataset. How much farther could you take machine analysis of music? Music has so much structure that's so apparent to our ears. Wouldn't it be cool to be able to parse out that structure algorithmically? Turns out, you can.
Apparently The International Society for Music Information Retrieval (ISMIR) is the place to go for this sort of thing. A few papers, based on minutes of rigorous research (aka random googling):
- Deriving Musical Structures from Signal Analysis for Music Audio Summary Generation
- Pattern Discovery Techniques for Music Audio
- Audio-based Music Structure Analysis
In addition to inferring a song's internal structure, you might want to relate it's acoustic features to styles, moods or time periods (as we did in the lab). For that, you'll want music metadata from sources like:
There's a paper on The Million Song Dataset paper by two researchers at Columbia's EE department and two more at the Echo Nest.
Even Google is interested in the topic: Sharing Learned Latent Representations For Music Audio Classification And Similarity.
Tangentially related, a group out of Cambridge and Stanford say Musical Preferences are Linked to Cognitive Styles. I fear what my musical tastes would reveal about my warped cognitive style.
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