Extracting behavioral information from animal sounds has long been a focus of research in bioacoustics, as sound-derived data are crucial for understanding animal behavior and environmental interactions. Traditional methods, which involve manual revi...
Modern artificial intelligence (AI) technology is capable of generating human sounding voices that could be used to deceive recipients in various contexts (e.g., deep fakes). Given the increasing accessibility of this technology and its potential soc...
BACKGROUND: The two most commonly used methods to identify frailty are the frailty phenotype and the frailty index. However, both methods have limitations in clinical application. In addition, methods for measuring frailty have not yet been standardi...
PURPOSE: Mental health screening is recommended by the US Preventive Services Task Force for all patients in areas where treatment options are available. Still, it is estimated that only 4% of primary care patients are screened for depression. The go...
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that can result in a progressive loss of speech due to bulbar dysfunction, which can have significant negative impact on the patient's mental well-being. Alternative Augmentative Comm...
As generative artificial intelligence (AI) continues its ballistic trajectory, everything from text to audio, image, and video generation continues to improve at mimicking human-generated content. Through a series of perceptual studies, we report on ...
BACKGROUND AND AIM: Parkinson's disease is a neurodegenerative disease. It is often diagnosed at an advanced stage, which can influence the control over the illness. Therefore, the possibility of diagnosing Parkinson's disease at an earlier stage, an...
The human voice stands out for its rich information transmission capabilities. However, voice communication is susceptible to interference from noisy environments and obstacles. Here, we propose a wearable wireless flexible skin-attached acoustic sen...
Machine learning approaches including deep learning models have shown promising performance in the automatic detection of Parkinson's disease. These approaches rely on different types of data with voice recordings being the most used due to the conve...