Adrian Huber
Position:
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PhD Student -- ended Jul 2019
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Email:
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Work phone:
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My interests lie primarily in signal processing and the mathematical theory thereof. Within the scope of the COCOHA project I am particularly interested in the question how signal mixtures can be effectively separated into their constituent components. Geometrical questions interest me as well. Research questions intricately tied to geometry are finding optimal placements of sensors in spatial configurations for localizing sound sources and for distributed beamforming. My aim is to develop a system for online source separation/streaming of data captured with multiple distributed microphones. In this context it is then necessary to study not just the correct behaviour of algorithms but also the computational burden they impose on hardware.
Publications
2019
Huber, A. and Liu, S-C. Filtering of nonuniformly sampled bandlimited functions, IEEE Signal Processing Letters, 2019
Huber, A., Anumula, J.,. and Liu, S-C. Optimal sampling of parametric families: Implications for machine learning, Neural Computation, 2019
Liu, Shih-Chii and Rueckauer, Bodo and Ceolini, Enea and Huber, Adrian and Delbruck, Tobi
Event-Driven Sensing for Efficient Perception: Vision and Audition Algorithms,
IEEE Signal Processing Magazine 29-37, 2019
2018
Anumula, J., Ceolini, E., He, Z., Huber, A., and Liu, S-C. An event-driven probabilistic model of sound source localization using cochlea spikes, 2018 IEEE International Symposium on Circuits and Systems, 2018
Ceolini, E. and Anumula, J. and Huber, A and Kiselev, I. and Liu, S-C. Speaker activity detection and minimum variance beamforming for source separation, 2018 Interspeech, 2018
Huber, A. and Liu, S-C. On approximation of bandlimited functions with compressed sensing, IEEE International Conference on Acoustics, Speech and Signal Processing, 2018