\invitedTalkHeader
{Machine Learning Methods for Music Discovery and Recommendation}
{Douglas Eck}
{Google Research}
{douglas.eck@gmail.com}
{research.google.com/}
{09:00--10:00}
{Wills, Great Hall}

\section*{Abstract}

In this talk I will relate current work at Google in music recommendation to the
challenge of automatic music annotation (``autotagging'').  I will spend
most of the talk looking at (a) signal processing and sparse coding
strategies for pulling relevant structure from audio, and (b) training
multi-class ranking models in order to build good music similarity
spaces.  Although I will describe some technical aspects of autotagging
and ranking via embedding, the main goal of the talk is to foster a
better understanding of the real-world challenges we face in helping
users find music they'll love. To this end I will play a number of audio
demos illustrating what we can (and cannot) hope to achieve by working
with audio.

\section*{Bio}

Douglas Eck is a research scientist at Google in Mountain View,
California.  His current focus is on machine learning models and user
interfaces for music discovery and recommendation.  This involves not
only algorithm development but also user studies and data analyses to
better understand what listeners want from a music service.  Before
coming to Google, Douglas was an associate professor in Computer
Science at University of Montreal where he worked in related areas
such as meter and beat induction, automatic tagging of music tracks
and expressive timing and dynamics in music performance.
