Paul Lamere: Data Mining Music
Paul Lamere explores "how we can use music data mining for tasks such as automatic genre detection, song similarity for music recommendation, and data visualization for music exploration and discovery."
| What |
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| When |
Feb 24, 2012 from 04:00 PM to 05:00 PM |
| Where | 35 W 4th St Room 610 New York, NY 10003 |
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Abstract
In this talk we dive into mega-scale music data such as the Million Song Dataset (a recently released, freely-available collection of detailed audio features and metadata for a million contemporary popular music tracks) to help us get a better understanding of the music and the artists that perform the music. We explore how we can use music data mining for tasks such as automatic genre detection, song similarity for music recommendation, and data visualization for music exploration and discovery. We use these techniques to try to answers questions about music such as: Which drummers use click tracks to help set the tempo? or Is music really faster and louder than it used to be? Finally, we look at techniques and challenges in processing these extremely large datasets.
Bio
Paul is the Director of Developer Platform for The Echo Nest, a music intelligence companylocated in Boston MA. Paul is interested in using data about music to help listeners explore for and discover new music. Paul spends his spare time attending MusicHack Days where he creates hacks such as the 'Music Maze', 'Six Degrees of Black Sabbath' and 'Bohemiam Rhapsichord'.
