  Deep learning-based speech enhancement for real-time applications recently
made large advancements. Due to the lack of a tractable perceptual optimization
target, many myths around training losses emerged, whereas the contribution to
success of the loss functions in many cases has not been investigated isolated
from other factors such as network architecture, features, or training
procedures. In this work, we investigate a wide variety of loss spectral
functions for a recurrent neural network architecture suitable to operate in
online frame-by-frame processing. We relate magnitude-only with phase-aware
losses, ratios, correlation metrics, and compressed metrics. Our results reveal
that combining magnitude-only with phase-aware objectives always leads to
improvements, even when the phase is not enhanced. Furthermore, using
compressed spectral values also yields a significant improvement. On the other
hand, phase-sensitive improvement is best achieved by linear domain losses such
as mean absolute error.
