Feature extraction and representation has significant impact on the performance of any
machine learning method. Mel Frequency Cepstrum Coefficient (MFCC) is designed to …

Voice is an essential component of human communication, serving as a fundamental
medium for expressing thoughts, emotions, and ideas. Disruptions in vocal fold vibratory …

Deep learning techniques such as convolutional neural networks (CNN) have been
successfully applied to identify pathological voices. However, the major disadvantage of …

In this paper, we present a study on the efficiency of neural networks for the hard problem of
automatically classifying voice disorders. To this end, convolutional architectures combined …

Machine learning has the potential to help optimize, assess, predict, and rehabilitate voice
and swallowing function in head and neck cancer patients as well as aid in cancer …

Goal: Numerous studies had successfully differentiated normal and abnormal voice
samples. Nevertheless, further classification had rarely been attempted. This study proposes …

Recently, huge concerns have been raised in diagnosing chest diseases, especially after
the COVID-19 pandemic. Regular diagnosis processes of chest diseases sometimes fail to …

The shortage and availability limitation of RT-PCR test kits and is a major concern regarding
the COVID-19 pandemic. The authorities' intention is to establish steps to control the …

Determining and classifying pathological human sounds are still an interesting area of
research in the field of speech processing. This paper explores different methods of voice …

In this study, the convolutional neural network (CNN) and multipart interactive training were
used to create a state-of-the-art classifier for the early detection of cardiac pathologies. The …