Frequency-domain monaural speech enhancement has been extensively studied for over
60 years, and a great number of methods have been proposed and applied to many …

The recent surge in the use of Deep Neural Networks (DNNs) has also made its mark in the
field of Audio Enhancement (AE), providing much better quality than the classical methods …

We propose the multi-head convolutional neural network (MCNN) for waveform synthesis
from spectrograms. Nonlinear interpolation in MCNN is employed with transposed …

Time-frequency (TF) representations provide powerful and intuitive features for the analysis
of time series such as audio. But still, generative modeling of audio in the TF domain is a …

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 …

For planetary gear has the characteristics of small volume, light weight and large
transmission ratio, it is widely used in high speed and high power mechanical system. Poor …

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

In recent years, text-to-speech (TTS) synthesis has benefited from advanced machine
learning approaches. Most prominently, since the introduction of the WaveNet architecture …

In this paper, we propose a phase reconstruction framework, named Deep Griffin-Lim
Iteration (DeGLI). Phase reconstruction is a fundamental technique for improving the quality …

Recovering a signal from its amplitude spectrogram, or phase recovery, exhibits many
applications in acoustic signal processing. When only an amplitude spectrogram is available …