  Music source separation with deep neural networks typically relies only on
amplitude features. In this paper we show that additional phase features can
improve the separation performance. Using the theoretical relationship between
STFT phase and amplitude, we conjecture that derivatives of the phase are a
good feature representation opposed to the raw phase. We verify this conjecture
experimentally and propose a new DNN architecture which combines amplitude and
phase. This joint approach achieves a better signal-to distortion ratio on the
DSD100 dataset for all instruments compared to a network that uses only
amplitude features. Especially, the bass instrument benefits from the phase
information.
