  We learn audio representations by solving a novel self-supervised learning
task, which consists of predicting the phase of the short-time Fourier
transform from its magnitude. A convolutional encoder is used to map the
magnitude spectrum of the input waveform to a lower dimensional embedding. A
convolutional decoder is then used to predict the instantaneous frequency
(i.e., the temporal rate of change of the phase) from such embedding. To
evaluate the quality of the learned representations, we evaluate how they
transfer to a wide variety of downstream audio tasks. Our experiments reveal
that the phase prediction task leads to representations that generalize across
different tasks, partially bridging the gap with fully-supervised models. In
addition, we show that the predicted phase can be used as initialization of the
Griffin-Lim algorithm, thus reducing the number of iterations needed to
reconstruct the waveform in the time domain.
