Abstract: For the past 60 years, machines have been involved in all aspects of music: playing, recording, processing, editing, mixing, composing, analyzing, and synthesizing. However, in software terms, music is nothing but a sequence of numbers and functions describing waveforms (what to play) and scores (when to play). It doesn't have a notion of what music sounds like, and how it is perceived and received by listeners, in its context, time and space. The Echo Nest is a music intelligence company that provides a deep and granular level of musical information at scale, on both content and context. By listening to every song (tempo, rhythm, timbre, harmony), and reading every piece of music text online (blog posts, news, reviews), the "musical brain" constantly learns to reverse engineer music. Its knowledge on 35 million unique songs and 2 million artists was generated automatically and dynamically over the past 6 years. Through many examples and live demos, we demonstrate the power of big-data driven software in the context of personalized listening experiences and music creation.

Bio: Tristan earned a doctorate in Media Arts and Sciences from MIT in 2005. His academic work combined machine listening and machine learning technologies in teaching computers how to hear and make music. He first earned an MS in Electrical Engineering and Computer Science from the University of Rennes in France, later working on music signal parameter extraction at the Center for New Music and Audio Technologies at U.C. Berkeley. He has worked with leading research and development labs in the U.S. and France as a software and hardware engineer in areas of machine listening and audio analysis. He is a co-founder and the Chief Science Officer of Music Intelligence company The Echo Nest, which powers smarter music applications for a wide range of customers including MTV, Spotify, The BBC, MOG, eMusic, Clear Channel, Rdio, EMI, and a community of more than 12,000 independent application developers.