Automatic modulation classification (AMC) is of crucial importance for realizing wireless intelligence communications. Many deep learning based models especially convolution neural networks (CNNs) have been proposed AMC. However, the computation cost very high, which makes them inappropriate beyond fifth generation communication that stringent requirements on accuracy and computing time. In ord...

A model is presented for the analysis and synthesis of low frequency human-like pitch deviation, as a replacement for existing modulation techniques in singing voice synthesis systems. Fundamental frequency (f0) measurements are taken from vocalists producing a selected range of utterances without vibrato and trends in the data are observed. A probabilistic function that provides natural soundi...

This work presents a novel approach for the automatic detection of pathological voices based on fusing the information extracted by means of mel-frequency cepstral coefficients (MFCC) and features derived from the modulation spectra (MS). The system proposed uses a two-stepped classification scheme. First, the MFCC and MS features were used to feed two different and independent classifiers; and...

Overlapped-speech is known to degrade performance in automatic speech systems. In this study, a sub-band speech analysis technique is proposed to detect overlapped-speech segments in single-channel multi-speaker scenarios (i.e., co-channel speech). Sub-band signals are obtained by decomposing the input speech using a gammatone filterbank. Filterbank outputs are then used to modulate the frequen...

Based on a comparative analysis of the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, we optimize structure GRU network propose new modulation recognition method based feature extraction deep learning algorithm. High-order cumulant, Signal-to-Noise Ratio (SNR), instantaneous feature, cyclic spectrum signals are extracted firstly, then input into Convolutional Neural Netw...