Our paper entitled “Are You Speaking with a Mask? An Investigation on Attention based Deep Temporal Convolutional Neural Networks for Mask Detection Task” was awarded the “Best Paper Award” jointly supported by the CMST and Frontiers.
We are pleased to announce that our recent work has been accepted by the IEEE Internet of Things Journal (IEEE IoTJ). The paper is entitled “Can Appliances Understand the Behaviour of Elderly via Machine Learning? A Feasibility Study”. Readers who are interested in AI for improving the life quality of the elderly group can refer to this paper.
We are organising the special session entitled “Computer Audition for Healthcare (CA4H)” in the upcoming ICASSP 2021. It is the first time attract contributions specialised in the topic on CA4H at the prestigious conference.
Our recent paper entitled “Can Machine Learning Assist Locating the Excitation of Snore Sound? A Review” is accepted and published online by IEEE Journal of Biomedical and Health Informatics (IEEE J-BHI, IF-2019: 5.223). Readers who may be interested in computer audition based methods for non/less invasive methods for snore site location can refer to this paper at: https://ieeexplore.ieee.org/document/9152123/authors#authors.
We have a new opinion paper entitled “Computer Audition for Healthcare: Opportunities and Challenges” accepted and published by Frontiers in Digital Health. We give our perspectives and insights for applications in biomedical and health informatics by leveraging the power of computer audition (CA).
We have a new paper entitled “An Online Robot Collision Detection and Identification Scheme by Supervised Learning and Bayesian Decision Theory” hase been accepted and published online by IEEE Transactions on Automation Science and Engineering (IEEE T-ASE, IF: 5.224). Readers who may have interests on AI for Robotic Control could refer this article.
We are pleased to announce that our recent paper, “Validity of Machine Learning in Biology and Medicine Increased Through Collaborations Across Fields of Expertise” was accepted and published online by Nature Machine Intelligence. We hope this review article can contribute a comprehensive summary of current work and future perspectives of machine learning for life sciences.
We are pleased that our recent paper ” Machine Listening for Heart Status Monitoring: Introducing and Benchmarking HSS — the Heart Sounds Shenzhen Corpus” has been accepted and pre-printed by IEEE Journal of Biomedical and Health Informatics (IEEE J-BHI, IF: 4.217). This is a milestone for our team research work on heart sound classification.
From November 7th, 2019 to November 10th, 2019, I visited Kyushu University (九州大学). It was a great experience! An amazing campus and a very happy talk with Prof. Keiji Iramina and his lab members. I gave a presentation on the topic of “A.I. Betters Our Life: Applications from General Audio to Wearable Data“, which introduced my doctoral study in Germany and my recent research work in Japan. I am very much looking forward to collaborating with the excellent colleagues in A.I. for BCI (brain-computer interface) and neurology sciences. Fukuoka is so beautiful!