Endowing machines with sensing capabilities similar to those of humans is a prevalent
quest in engineering and computer science. In the pursuit of making computers sense their …

This paper introduces a methodology based on deep convolutional neural networks (DCNN)
for motor imagery (MI) tasks recognition in the brain-computer interface (BCI) system. More …

A deep learning-based method is proposed for jointly detecting and localizing speech
sources in a complex acoustic scene by using microphones of a hearing aid. Motivated by …

Signal multiscale decomposition (SMD) is an effective analysis for the identification of modal
information in time-domain signals. So far, various SMD approaches, such as the …

We develop noise robust features using Gammatone wavelets derived from the popular
Gammatone functions. These wavelets incorporate the characteristics of human peripheral …

Homo-sapiens suffer from psychogenic pain due to current day lifestyle. According to
psychologists, s tress is the most destructive form of psychalgia and it is a vicious companion …

Illegal logging is one of the major causes of adverse environmental effects such as
deforestation, loss of biodiversity, and emission of greenhouse gases. Therefore, illegal …

ABSTRACT We introduce a Moment Matching-Scattering Inverse Network (MM-SIN) to
generate and transform musical sounds. The MM-SIN generator is similar to a variational …

This dissertation addresses audio classification by designing signal representations which
satisfy appropriate invariants while preserving inter-class variability. First, we study time …

The emerging field of computational acoustic monitoring aims at retrieving high-level
information from acoustic scenes recorded by some network of sensors. These networks …