A limited number of types of sound event occur in an acoustic scene and some sound events tend to co-occur in the scene; for example, the sound events "dishes" and "glass jingling" are likely to co-occur in the acoustic scene "cooking". In this paper, we propose a method of sound event detection using graph Laplacian regularization with sound event co-occurrence taken into account. In the proposed method, the occurrences of sound events are expressed as a graph whose nodes indicate the frequencies of event occurrence and whose edges indicate the sound event co-occurrences. This graph representation is then utilized for the model training of sound event detection, which is optimized under an objective function with a regularization term considering the graph structure of sound event occurrence and co-occurrence. Evaluation experiments using the TUT Sound Events 2016 and 2017 detasets, and the TUT Acoustic Scenes 2016 dataset show that the proposed method improves the performance of sound event detection by 7.9 percentage points compared with the conventional CNN-BiGRU-based detection method in terms of the segment-based F1 score. In particular, the experimental results indicate that the proposed method enables the detection of co-occurring sound events more accurately than the conventional method.
An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection
Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey（萨里大学视觉、语音与信号处理中心（CVSSP））；ByteDance（字节跳动）；Qingdao University of Technology（青岛理工大学）
Ensemble Confidence Calibration for Sound Event Detection in Open-environment
School of Computer Science and Technology, Harbin University of Science and Technology（哈尔滨理工大学计算机科学与技术学院）；School of Electrical Engineering, KAIST（韩国成均馆大学电子工程学院）
RealDESED: A Real-World Domestic Sound Event Detection Benchmark
Institute of Computational Perception, Johannes Kepler University Linz, Austria（计算感知研究所，约翰尼斯·开普勒大学林茨，奥地利）；LIT Artificial Intelligence Lab, Linz, Austria（LIT人工智能实验室，林茨，奥地利）
In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning
Advanced Remanufacturing and Technology Centre (ARTC)（先进再制造与技术中心（ARTC））；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与宇航工程学院）；Singapore Institute of Manufacturing Technology (SIMTech)（新加坡制造技术研究院（SIMTech））；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）
