The importance of automated and objective monitoring of dietary behavior is becoming increasingly accepted. The advancements in sensor technology along with recent achievements in machine-learning--based signal-processing algorithms have enabled the development of dietary monitoring solutions that yield highly accurate results. A common bottleneck for developing and training machine learning algorithms is obtaining labeled data for training supervised algorithms, and in particular ground truth annotations. Manual ground truth annotation is laborious, cumbersome, can sometimes introduce errors, and is sometimes impossible in free-living data collection. As a result, there is a need to decrease the labeled data required for training. Additionally, unlabeled data, gathered in-the-wild from existing wearables (such as Bluetooth earbuds) can be used to train and fine-tune eating-detection models. In this work, we focus on training a feature extractor for audio signals captured by an in-ear microphone for the task of eating detection in a self-supervised way. We base our approach on the SimCLR method for image classification, proposed by Chen et al. from the domain of computer vision. Results are promising as our self-supervised method achieves similar results to supervised training alternatives, and its overall effectiveness is comparable to current state-of-the-art methods. Code is available at https://github.com/mug-auth/ssl-chewing .
Echo Detection in Spatial Room Impulse Responses Measured with Spherical Microphone Arrays Using the Herglotz Wavefunction
STMS, Ircam–Sorbonne Université–CNRS–Ministère de la Culture（STMS，Ircam-索邦大学-CNRS-文化部）；Acoustics Research Institute, Austrian Academy of Sciences（奥地利科学院声学研究所）
Active noise cancellation on open-ear smart glasses
Department of Electrical and Computer Engineering, Carnegie Mellon University（卡内基梅隆大学电气与计算机工程系）；Department of Medical Physics and Acoustics, Carl von Ossietzky Universität Oldenburg（奥尔登堡大学医学物理与声学系）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
ABSE-NET: A Lightweight Neural Model for Active Binaural Speech Enhancement in Open-Fit Hearing Aids
College of Computer Science, Inner Mongolia University（内蒙古大学计算机学院）；College of Electronic and Information Engineering, Inner Mongolia University（内蒙古大学电子与信息工程学院）
Atrial Fibrillation Detection System via Acoustic Sensing for Mobile Phones
Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）；Southern University of Science and Technology（南方科技大学）；Southern University of Science and Technology Hospital（南方科技大学医院）
AcousAF: Acoustic Sensing-Based Atrial Fibrillation Detection System for Mobile Phones
Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）；Southern University of Science and Technology（南方科技大学）；Southern University of Science and Technology Hospital（南方科技大学医院）
