Source Separation Tutorial Mini-Series I: Classical Speech Denoising and Enhancement
Abstract: To start off a series of three tutorial-style dsp seminars on current single-channel source separation methods, the first talk will introduce the topic of classical methods used for speech enhancement. We will cover denoising methods such as spectral subtraction, wiener filters, and probabilistic estimators (if time permits). This will give an overview of the large range of literature on reducing background noise from speech.
Given the nature of the application, most of these methods have to work in real-time settings and have to be robust to non-stationary noisy environments. We will discuss some of the advantages and shortcomings of these methods in real-world scenarios when it comes to enhancing noisy speech. Following the lecture, we will also get a chance to program some of these speech enhancement algorithms and see how they perform under various settings. 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 Bio: Eunjoon Cho is a PhD candidate in the Department of Electrical Engineering at Stanford. His research focus is on using the underlying structure of speech to estimate background noise in non-stationary environments.