  Complex-valued processing brought deep learning-based speech enhancement and
signal extraction to a new level.
  Typically, the noise reduction process is based on a time-frequency (TF) mask
which is applied to a noisy spectrogram. Complex masks (CM) usually outperform
real-valued masks due to their ability to modify the phase.
  Recent work proposed to use a complex linear combination of coefficients
called complex linear coding (CLC) instead of a point-wise multiplication with
a mask.
  This allows to incorporate information from previous and optionally future
time steps which results in superior performance over mask-based enhancement
for certain noise conditions.
  In fact, the linear combination enables to model quasi-steady properties like
the spectrum within a frequency band.
  In this work, we apply CLC to the Deep Noise Suppression (DNS) challenge and
propose CLC as an alternative to traditional mask-based processing, e.g. used
by the baseline.
  We evaluated our models using the provided test set and an additional
validation set with real-world stationary and non-stationary noises.
  Based on the published test set, we outperform the baseline w.r.t. the scale
independent signal distortion ratio (SI-SDR) by about 3dB.
