This paper describes a novel text-to-speech (TTS) technique based on deep convolutional
neural networks (CNN), without use of any recurrent units. Recurrent neural networks (RNN) …

Setting up indoor localization systems is often excessively time-consuming and labor-
intensive, because of the high amount of anchors to be carefully deployed or the …

A noniterative method for the reconstruction of the short-time fourier transform (STFT) phase
from the magnitude is presented. The method is based on the direct relationship between …

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

In this paper, we propose the neural homomorphic vocoder (NHV), a source-filter model
based neural vocoder framework. NHV synthesizes speech by filtering impulse trains and …

Abstract This paper proposes Discrete Cosine Transform (DCT) based speech
enhancement algorithms. These algorithms utilize minimum mean square error (MMSE) …

The objectives of the dysarthria assessment are to discriminate dysarthric speech from
normal speech, to estimate the severity of dysarthria in terms of the dysarthric speech …

Phase reconstruction from amplitude spectrograms has attracted attention in recent
acoustics because of its potential applications in speech synthesis and enhancement. The …

Time-frequency masking is a common solution for the single-channel source separation
(SCSS) problem where the goal is to find a time-frequency mask that separates the …

The emergence of deep neural networks has made speech enhancement well developed.
Most of the early models focused on estimating the magnitude of spectrum while ignoring …