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<p><span style='font-family: "Helvetica Neue"; font-size: 13px;'>Dear colleagues,<br>
<br>
we welcome your paper submissions to our Research Topic on</span> <span style='font-family: "Helvetica Neue"; font-size: 13px;'><strong>Intelligent Audition Technologies for Personalized Healthcare</strong>.</span><br>
<br>
https://www.frontiersin.org/research-topics/54868/intelligent-audition-technologies-for-personalized-healthcare<br>
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<strong style='font-family: "Helvetica Neue"; font-size: 13px;'>Manuscript Submission Deadline:</strong><span style='font-family: "Helvetica Neue"; font-size: 13px;'> </span><br style='font-family: "Helvetica Neue"; font-size: 13px;'>
<span style='font-family: "Helvetica Neue"; font-size: 13px;'>31 December 2023</span><br>
<br>
<span style='font-family: "Helvetica Neue"; font-size: 13px;'><strong>Participating Journals:</strong></span><br></p>
<p style='margin: 0px; font-stretch: normal; font-size: 13px; line-height: normal; font-family: "Helvetica Neue"; color: rgb(220, 161, 13);'><a href="https://www.frontiersin.org/journals/1511"><span style="color:#000000;">Frontiers in Computer Science</span></a><span style="color:#000000;">,</span> <a href="https://www.frontiersin.org/journals/1380"><span style="color:#000000;">Frontiers in Big Data</span></a><span style="color:#000000;">,</span> <a href="https://www.frontiersin.org/journals/1437"><span style="color:#000000;">Frontiers in Artificial Intelligence</span></a><span style="color:#000000;">,</span> <a href="https://www.frontiersin.org/journals/1534"><span style="color:#000000;">Frontiers in Digital Health</span></a></p>
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<p style='margin: 0px; font-stretch: normal; font-size: 13px; line-height: normal; font-family: "Helvetica Neue";'><br>
<strong>Topic Editors:</strong><br>
Zhao Ren, University of Bremen, Germany<br>
Tanja Schultz, University of Bremen, Germany<br>
Kun Qian, Beijing Institute of Technology, China,<br>
Björn W. Schuller, Technical University of Munich & Imperial College London, Germany & UK<br>
</p>
<p><span style='font-family: "Helvetica Neue"; font-size: 13px;'>*********************************************<br>
Over the past few decades, artificial intelligence has demonstrated its potential in helping with healthcare applications, including intelligent diagnosis, decision-making, and treatments using large amounts of medical data. Benefitting from signal processing, machine learning, and deep learning algorithms, computer audition is now rapidly emerging as an innovative instrument for effective, efficient, affordable, and non-invasive medical services using body acoustics and human speech. The new evolution of Internet-of-Things further increased the possibility of creating personalized healthcare with intelligent audition technologies, which can provide human-centered healthcare applications. The tendency toward developing machine learning and deep learning techniques in computer audition for personalized healthcare is promising and encouraging.</span></p>
<p style='margin: 0px; font-stretch: normal; font-size: 13px; line-height: normal; font-family: "Helvetica Neue";'><br>
This Research Topic seeks to progress the interdisciplinary field of healthcare, artificial intelligence, and acoustic signal processing by facilitating the investigation of advanced methodologies and encouraging the development of new applications. The goal is to gather innovative works, including not only computer-audition prototypes for healthcare, but also cutting-edge machine-learning methodologies. This topic is finally expected to promote the development of trustworthy, dependable, sustainable artificial intelligence algorithms for personalized healthcare. This Research Topic is also thought of as a venue for audiences from the signal processing and computer science communities, as well as medical groups.<br>
<br>
The Research Topic is looking for qualified research works in the field of intelligent audition technologies for personalized healthcare. Topics of interest include, but are not limited to:<br>
<br>
- Prototypes of computer audition for healthcare applications<br>
- New acoustic databases for study in healthcare<br>
- Trustworthy machine learning approaches for computer audition in the healthcare sector<br>
- Adversarial machine learning in computer audition for healthcare<br>
- Dependable machine learning methods in computer audition for healthcare<br>
- Methodologies and discussion in sustainability of machine learning for computer audition in the healthcare sector<br>
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<br>
Best regards,<br>
Zhao Ren</p>
<div><span style="font-family:arial,helvetica,sans-serif;">Dr.-Ing. Zhao Ren</span><br>
<span style="font-family: arial, helvetica, sans-serif;">Cognitive Systems Lab (CSL)</span><br>
University of Bremen<br>
<span style="font-family:arial,helvetica,sans-serif;">Germany</span></div>
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