  We introduce a new audio processing technique that increases the sampling
rate of signals such as speech or music using deep convolutional neural
networks. Our model is trained on pairs of low and high-quality audio examples;
at test-time, it predicts missing samples within a low-resolution signal in an
interpolation process similar to image super-resolution. Our method is simple
and does not involve specialized audio processing techniques; in our
experiments, it outperforms baselines on standard speech and music benchmarks
at upscaling ratios of 2x, 4x, and 6x. The method has practical applications in
telephony, compression, and text-to-speech generation; it demonstrates the
effectiveness of feed-forward convolutional architectures on an audio
generation task.
