Source Separation Tutorial Mini-Series II: Introduction to Non-Negative Matrix Factorization
Abstract: For the second talk in this series, we will introduce the topic of non-negative matrix factorization for the purpose of single-channel source separation. NMF is one of the current most promising and effective class of approaches found for source separation and is a popular topic in several signal processing conferences and journals. Following the lecture, we will get a chance to program a basic source separator. Please bring your laptops with Matlab and/or Octave installed and be ready to code!
Slides + Code @ https://ccrma.stanford.edu/~njb/teaching/sstutorial/
Speaker Bios: Dennis Sun is a PhD candidate in the Department of Statistics and working with Prof. Jonathan Taylor. His research interests are at the interface of statistics, signal processing, machine learning, and musicology.
Nicholas J. Bryan is a PhD candidate at the Center for Computer Research in Music and Acoustics (CCRMA) working with Prof. Ge Wang. His research interests are at the intersection between signal processing, machine learning, and human-computer interaction.