A Compact and Discriminative Feature Based on Auditory Summary Statistics for Acoustic Scene Classification

One of the biggest challenges of acoustic scene classification (ASC) is to find proper features to better represent and characterize environmental sounds. Environmental sounds generally involve more sound sources while exhibiting less structure in temporal spectral representations. However, the background of an acoustic scene exhibits temporal homogeneity in acoustic properties, suggesting it could be characterized by distribution statistics rather than temporal details. In this work, we investigated using auditory summary statistics as the feature for ASC tasks. The inspiration comes from a recent neuroscience study, which shows the human auditory system tends to perceive sound textures through time-averaged statistics. Based on these statistics, we further proposed to use linear discriminant analysis to eliminate redundancies among these statistics while keeping the discriminative information, providing an extreme com-pact representation for acoustic scenes. Experimental results show the outstanding performance of the proposed feature over the conventional handcrafted features.Comment: Accepted as a conference paper of Interspeech 201

Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events

In this paper, we propose a new strategy for acoustic scene classification (ASC) , namely recognizing acoustic scenes through identifying distinct sound events. This differs from existing strategies, which focus on characterizing global acoustical distributions of audio or the temporal evolution of short-term audio features, without analysis down to the level of sound events. To identify distinct sound events for each scene, we formulate ASC in a multi-instance learning (MIL) framework, where each audio recording is mapped into a bag-of-instances representation. Here, instances can be seen as high-level representations for sound events inside a scene. We also propose a MIL neural networks model, which implicitly identifies distinct instances (i.e., sound events). Furthermore, we propose two specially designed modules that model the multi-temporal scale and multi-modal natures of the sound events respectively. The experiments were conducted on the official development set of the DCASE2018 Task1 Subtask B, and our best-performing model improves over the official baseline by 9.4% (68.3% vs 58.9%) in terms of classification accuracy. This study indicates that recognizing acoustic scenes by identifying distinct sound events is effective and paves the way for future studies that combine this strategy with previous ones.Comment: code URL typo, code is available at https://github.com/hackerekcah/distinct-events-asc.gi

Combustion and Exhaust Emission Characteristics of Low Swirl Injector

This document is the Accepted Manuscript version of the following article: Yangbo Deng, Hongwei Wu, and Fengmin Su, ‘Combustion and exhaust emission characteristics of low swirl injector’, Applied Thermal Engineering, Vol. 110, pp. 171-180, first published online 28 August 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ The version of record is available online at doi: http://dx.doi.org/10.1016/j.applthermaleng.2016.08.169 © 2016 Elsevier Ltd. All rights reserved.The present experimental study aims to investigate the combustion and emission characteristics of the flow through a low swirl injector (LSI). An experimental study was carried out on the flame structure, the temperature distribution and the exhaust emission of low swirl pre-mixed combustion under the condition of different swirl number and different fuel composition. In order to qualitatively analyze the flame structure, the velocity distribution of the non-reacting flow through the LSI was measured using the particle image velocimetry (PIV) technique. Experimental results indicated that: (i) the LSI can generate a blue lift-off “W” type flame which consists of four clusters of flames connected together and holds up a long yellow pulsating flame, (ii) the blue flame structure converts the “W” type flame into the “broom” type flame and the distance between the front of the flame and the nozzle shortens with increasing swirl number, (iii) there exist high temperature region flanked by two peaks on the temperature profiles in the blue flame while uniform higher temperature in yellow pulsating flame, (iv) the NOx and CO emission level of the LSI mainly depends on the gas composition and thermal load.Peer reviewe

Dynamic Characteristic Analysis of Large Space Deployable Articulated Mast

AbstractEquivalent continuum beam model and finite element model of space deployable articulated mast are developed. Dynamic characteristics of the mast without and with tip load are analyzed based on two models respectively. Frequencies and mode shapes of the mast are calculated and simulated by Euler beam theory and finite element simulation, and theoretical solutions are compared with simulation results. Influence of mast structural parameters on mast natural frequencies is analyzed, the sensitivities of mast bending, torsional, axial frequencies to structural parameters are calculated. The measure for improving mast dynamic characteristic is proposed through sensitivity analysis

TeCH: Text-guided Reconstruction of Lifelike Clothed Humans

Despite recent research advancements in reconstructing clothed humans from a single image, accurately restoring the "unseen regions" with high-level details remains an unsolved challenge that lacks attention. Existing methods often generate overly smooth back-side surfaces with a blurry texture. But how to effectively capture all visual attributes of an individual from a single image, which are sufficient to reconstruct unseen areas (e.g., the back view)? Motivated by the power of foundation models, TeCH reconstructs the 3D human by leveraging 1) descriptive text prompts (e.g., garments, colors, hairstyles) which are automatically generated via a garment parsing model and Visual Question Answering (VQA), 2) a personalized fine-tuned Text-to-Image diffusion model (T2I) which learns the "indescribable" appearance. To represent high-resolution 3D clothed humans at an affordable cost, we propose a hybrid 3D representation based on DMTet, which consists of an explicit body shape grid and an implicit distance field. Guided by the descriptive prompts + personalized T2I diffusion model, the geometry and texture of the 3D humans are optimized through multi-view Score Distillation Sampling (SDS) and reconstruction losses based on the original observation. TeCH produces high-fidelity 3D clothed humans with consistent & delicate texture, and detailed full-body geometry. Quantitative and qualitative experiments demonstrate that TeCH outperforms the state-of-the-art methods in terms of reconstruction accuracy and rendering quality. The code will be publicly available for research purposes at https://huangyangyi.github.io/TeCHComment: Project: https://huangyangyi.github.io/TeCH, Code: https://github.com/huangyangyi/TeC

New Protections for Healthcare Workers

<p>Supplemental material, Video 1: sj-vid-1-pic-10.1177 0954406218779612 for Self-adaptive grasp analysis of a novel under-actuated cable-truss robotic finger by Shangling Qiao, Hongwei Guo, Rongqiang Liu, Yong Huang and Zongquan Deng in Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science</p

Multi-authority attribute-based keyword search over encrypted cloud data

National Research Foundation (NRF) Singapore; AXA Research Fun