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Patrick Hanebrink

2 accepted papers

2017

Beamnet: End-to-end training of a beamformer-supported multi-channel ASR system

ICASSP 2017accepted

This paper presents an end-to-end training approach for a beamformer-supported multi-channel ASR system. A neural network which estimates masks for a statistically optimum beamformer is jointly trained with a network for acoustic modeling. To update its parameters, we propagate the gradients from th…

Cited by 0SourceScholar
2017

Optimizing neural-network supported acoustic beamforming by algorithmic differentiation

ICASSP 2017accepted

In this paper we show how a neural network for spectral mask estimation for an acoustic beamformer can be optimized by algorithmic differentiation. Using the beamformer output SNR as the objective function to maximize, the gradient is propagated through the beamformer all the way to the neural netwo…

Cited by 0SourceScholar