  While machine learning techniques are traditionally resource intensive, we
are currently witnessing an increased interest in hardware and energy efficient
approaches. This need for resource-efficient machine learning is primarily
driven by the demand for embedded systems and their usage in ubiquitous
computing and IoT applications. In this article, we provide a
resource-efficient approach for multi-channel speech enhancement based on Deep
Neural Networks (DNNs). In particular, we use reduced-precision DNNs for
estimating a speech mask from noisy, multi-channel microphone observations.
This speech mask is used to obtain either the Minimum Variance Distortionless
Response (MVDR) or Generalized Eigenvalue (GEV) beamformer. In the extreme case
of binary weights and reduced precision activations, a significant reduction of
execution time and memory footprint is possible while still obtaining an audio
quality almost on par to single-precision DNNs and a slightly larger Word Error
Rate (WER) for single speaker scenarios using the WSJ0 speech corpus.
