Speaker "Erik Schmidt" Details Back



Machine Listening at Pandora


Finding the music of the moment can often be a challenging problem even for humans with well-versed musical tastes. These challenges further explode into myriad complexities when attempting to construct algorithmic approaches for automatic playlist generation. A variety of factors play a role in influencing a listeners perception of what music is appropriate on a given seed (e.g., musicological, social, geographical, generational), and these factors vary across different contexts and listeners. Furthermore, as opposed to more traditional recommender systems which need only to recommend a single item or set of items, Pandora’s recommenders must provide an evolving set of sequential items which constantly keep the experience new and exciting. The talk will present an overview of recommendation at Pandora, followed by a deep dive into the development of powerful machine listening systems which leverage the vast data resources of the MGP. I will discuss the extraction of acoustic feature representations, supervised machine learning, and additionally provide some insights into the application of listener feedback to develop powerful content-based recommender systems.


Erik M. Schmidt is a Senior Scientist at Pandora. Before joining Pandora he was a Post-Doctoral Researcher in the Music and Entertainment Technology Laboratory (MET-lab) at Drexel University in Philadelphia, PA. He received the Ph.D. degree from Drexel University in 2012, and also holds a Master's in Electrical Engineering from Drexel University and a Bachelor's from Temple University. His current research focuses on the leveraging explicit user feedback for the construction of powerful content-based exploration algorithms. Erik has general research interests in the areas of signal processing and machine learning for machine understanding of music audio.