The utilization of deep learning techniques in generating various contents (such as image,
text, etc.) has become a trend. Especially music, the topic of this paper, has attracted …

A description of technology targets in signal compression and a nonexhaustive account of
research directions that may lead toward these targets are presented. Opportunities for …

We survey a number of packet loss recovery techniques for streaming audio applications
operating using IP multicast. We begin with a discussion of the loss and delay characteristics …

In this article, we study the ability of deep neural networks (DNNs) to restore missing audio
content based on its context, ie, inpaint audio gaps. We focus on a condition which has not …

An apparatus and method for reducing co-channel interfer ence in multiple-access cellular
communication systems in which frame time or frequency slots are allocated between uplink …

In digital speech communication over noisy channels there is the need for reducing the
subjective effects of residual bit errors which have not been eliminated by channel decoding …

The quality of service limitation of today's Internet is a major challenge for real-time voice
communications. Excessive delay, packet loss, and high delay jitter all impair the …

In this article, we introduce GACELA, a conditional generative adversarial network (cGAN)
designed to restore missing audio data with durations ranging between hundreds of …

Echo state networks (ESNs) are a novel approach to recurrent neural network training with
the advantage of a very simple and linear learning algorithm. It has been demonstrated that …

Audio inpainting aims to reconstruct missing segments in corrupted recordings. Most of
existing methods produce plausible reconstructions when the gap lengths are short, but …