The sense of hearing in humans extends far beyond peripheral auditory function (i.e., audibility) as several factors such as auditory processing, cognitive function, listening effort, and affective responses are key to determine the individual response to sounds and speech communication abilities. Recent trends in data analytics, including advances in signal processing, image processing, machine learning, and deep learning are now opening unprecedented opportunities for a better understanding of human hearing through the analysis of measures taken all along the auditory pathway as well as in the behavioral domain. Unlocking the potential of data through knowledge extraction approaches can support research and clinical practice and represents a key step towards new, individualized approaches to auditory rehabilitation that are able to address the full picture of the individual listening and communication experiences.
The goal of this Research Topic is to address the latest advancements in data analytics as applied to hearing-related measures, including objective and behavioral measures, to help researchers and clinicians share the latest knowledge and support learning and exchange across disciplines. Such a transversal approach, not focused on a given aspect of hearing, will bring together knowledge about a variety of components of hearing and help broaden the adoption of advanced computational approaches to extract knowledge from the growing amount of data collected from clinical instrumentation, subjective measures, as well as mobile and wearable technology.
This collection aims to attract original research articles that advance the state of the art in data analytics and knowledge discovery techniques in the hearing field. Review articles that provide in-depth analyses of trending topics or identify novel research areas are also welcomed.
Topics of interest to this Research Topic include but are not limited to:
• Computational approaches for modeling human hearing and cognition
• Advances in image processing techniques for a better understanding of the central aspects of hearing
• New approaches for the analysis of the effects of hearing loss on cognitive decline
• Machine learning and deep learning algorithms for the analysis of hearing-related data
• New data analytics approaches for the investigation of tinnitus
• Advanced speech processing algorithms for hearing aids and auditory implants
• Novel computational methods for improved auditory neural stimulation
The sense of hearing in humans extends far beyond peripheral auditory function (i.e., audibility) as several factors such as auditory processing, cognitive function, listening effort, and affective responses are key to determine the individual response to sounds and speech communication abilities. Recent trends in data analytics, including advances in signal processing, image processing, machine learning, and deep learning are now opening unprecedented opportunities for a better understanding of human hearing through the analysis of measures taken all along the auditory pathway as well as in the behavioral domain. Unlocking the potential of data through knowledge extraction approaches can support research and clinical practice and represents a key step towards new, individualized approaches to auditory rehabilitation that are able to address the full picture of the individual listening and communication experiences.
The goal of this Research Topic is to address the latest advancements in data analytics as applied to hearing-related measures, including objective and behavioral measures, to help researchers and clinicians share the latest knowledge and support learning and exchange across disciplines. Such a transversal approach, not focused on a given aspect of hearing, will bring together knowledge about a variety of components of hearing and help broaden the adoption of advanced computational approaches to extract knowledge from the growing amount of data collected from clinical instrumentation, subjective measures, as well as mobile and wearable technology.
This collection aims to attract original research articles that advance the state of the art in data analytics and knowledge discovery techniques in the hearing field. Review articles that provide in-depth analyses of trending topics or identify novel research areas are also welcomed.
Topics of interest to this Research Topic include but are not limited to:
• Computational approaches for modeling human hearing and cognition
• Advances in image processing techniques for a better understanding of the central aspects of hearing
• New approaches for the analysis of the effects of hearing loss on cognitive decline
• Machine learning and deep learning algorithms for the analysis of hearing-related data
• New data analytics approaches for the investigation of tinnitus
• Advanced speech processing algorithms for hearing aids and auditory implants
• Novel computational methods for improved auditory neural stimulation