Auscultation is the most efficient way to diagnose cardiovascular and respiratory diseases.
To reach accurate diagnoses, a device must be able to recognize heart and lung sounds …

Conventional deep neural network (DNN)-based speech enhancement (SE) approaches
aim to minimize the mean square error (MSE) between enhanced speech and clean …

In the Internet of Things system, including wireless sensor networks, energy savings are a
matter of prime concern today. While electronic devices as well as software have been …

To increase transfer and storage efficiencies of the information, data compression has
emerged as a significant issue in the communication environments. This paper introduces …

This study focuses on identifying effective features for controlling speech to increase speech
intelligibility under adverse conditions. Previous approaches either cancel noise throughout …

The real-time performance of Automatic Speech Recognition (ASR) is a big challenge and
needs high computing capability and exhaustive memory consumption. Getting a robust …

Voice-related online communication applications are vulnerable to disruptions from complex
environments, such as packet loss in IP-switch channels and ambient noise, which hinder …

The diagnostic accuracy (ACC) of otitis media with effusion (OME) depends on a clinician's
experience and evaluation tools. Various assessment technologies have been applied to …

While traditional electrocardiogram (ECG) monitoring provides vital clinical information, its
electrode-based setup restricts patient movement. To address this limitation, contactless …

In this paper, we propose a novel speech enhancement (SE) method by exploiting the
discrete wavelet transform (DWT). This new method reduces the amount of fast time-varying …