Speaker: Emmanuel Vincent
Date: January 14, 2016
Audio signal processing has long been the obvious approach to problems such as microphone array processing, active noise control, or speech enhancement. Yet, it is increasingly being challenged by black-box machine learning approaches based on, e.g., deep neural networks (DNN), which have already achieved superior results on certain tasks. In this talk, I will try to convince that machine learning approaches shouldn’t be disregarded, but that black boxes won’t solve these problems either. There is hence an opportunity for signal processing researchers to join forces with machine learning researchers and solve these problems together. I will provide examples of this multi-disciplinary approach for audio source separation and robust automatic speech recognition.
Note: The talk was also given as a keynote speech at WASPAA 2015.