Our research focuses on the derivation and development of novel digital signal processing and machine learning algorithms for speech, audio, and multimodal signals. Addressed applications include communication devices such as hearing aids and mobile phones, as well as human-machine interfaces such as voice controlled assistants and robots. We aim at finding optimal solutions using mathematical and computational statistics for signal analysis and signal processing given a set of practical constraints. These constraints may include the low algorithmic latency in communications devices, limited computational power in mobile devices, and limited resources for training. The employed methods include Bayesian models and estimation, as well as machine learning techniques such as Artificial Deep Neural Networks. More Information about our research can be found here.
The following videos show our real-time source separation and speech enhancement demos in our varechoic sound studio.