activity
20172022
most citedA Novel Adaptive Kernel for the RBF Neural Networks

44 citations · 66 across the 11 of their papers we have counts for

collaborators

15 papers

eess.AS2022

End-to-End Lyrics Recognition with Self-supervised Learning

Xiangyu Zhang, Shuyue Stella Li, Zhanhong He +2

Lyrics recognition is an important task in music processing. Despite traditional algorithms such as the hybrid HMM- TDNN model achieving good performance, studies on applying end-t…

eess.AS2022

Automated Sex Classification of Children's Voices and Changes in Differentiating Factors with Age

Fuling Chen, Roberto Togneri, Murray Maybery +1

Sex classification of children's voices allows for an investigation of the development of secondary sex characteristics which has been a key interest in the field of speech analysi…

cs.IT2021

A Novel Quantum Calculus-based Complex Least Mean Square Algorithm (q-CLMS)

Alishba Sadiq, Imran Naseem, Shujaat Khan +3

In this research, a novel adaptive filtering algorithm is proposed for complex domain signal processing. The proposed algorithm is based on Wirtinger calculus and is called as q-Co…

cs.LG20212 cited

q-RBFNN:A Quantum Calculus-based RBF Neural Network

Syed Saiq Hussain, Muhammad Usman, Taha Hasan Masood Siddique +3

In this research a novel stochastic gradient descent based learning approach for the radial basis function neural networks (RBFNN) is proposed. The proposed method is based on the…

eess.AS2020

Multi-task Learning Based Spoofing-Robust Automatic Speaker Verification System

Yuanjun Zhao, Roberto Togneri, Victor Sreeram

Spoofing attacks posed by generating artificial speech can severely degrade the performance of a speaker verification system. Recently, many anti-spoofing countermeasures have been…

cs.LG2020

Multi-Kernel Fusion for RBF Neural Networks

Syed Muhammad Atif, Shujaat Khan, Imran Naseem +2

A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks. The current generatio…