  In this paper, we address the problem of reconstructing a time-domain signal
(or a phase spectrogram) solely from a magnitude spectrogram. Since magnitude
spectrograms do not contain phase information, we must restore or infer phase
information to reconstruct a time-domain signal. One widely used approach for
dealing with the signal reconstruction problem was proposed by Griffin and Lim.
This method usually requires many iterations for the signal reconstruction
process and depending on the inputs, it does not always produce high-quality
audio signals. To overcome these shortcomings, we apply a learning-based
approach to the signal reconstruction problem by modeling the signal
reconstruction process using a deep neural network and training it using the
idea of a generative adversarial network. Experimental evaluations revealed
that our method was able to reconstruct signals faster with higher quality than
the Griffin-Lim method.
