  This paper presents a novel phase reconstruction method (only from a given
amplitude spectrogram) by combining a signal-processing-based approach and a
deep neural network (DNN). To retrieve a time-domain signal from its amplitude
spectrogram, the corresponding phase is required. One of the popular phase
reconstruction methods is the Griffin-Lim algorithm (GLA), which is based on
the redundancy of the short-time Fourier transform. However, GLA often involves
many iterations and produces low-quality signals owing to the lack of prior
knowledge of the target signal. In order to address these issues, in this
study, we propose an architecture which stacks a sub-block including two
GLA-inspired fixed layers and a DNN. The number of stacked sub-blocks is
adjustable, and we can trade the performance and computational load based on
requirements of applications. The effectiveness of the proposed method is
investigated by reconstructing phases from amplitude spectrograms of speeches.
