In time-cost scale model studies, predicting acoustic performance by using simulation methods is a commonly used method that is preferred. In this field, building acoustic simulation tools are complicated by several challenges, including the high cost of acoustic tools, the need for acoustic expertise, and the time-consuming process of acoustic simulation. The goal of this project is to introduce a simple model with a short calculation time to estimate the room acoustic condition in the early design stages of the building. This paper presents a working prototype for a new method of machine learning (ML) to approximate a series of typical room acoustic parameters using only geometric data as input characteristics. A novel dataset consisting of acoustical simulations of a single room with 2916 different configurations are used to train and test the proposed model. In the stimulation process, features that include room dimensions, window size, material absorption coefficient, furniture, and shading type have been analysed by using Pachyderm acoustic software. The mentioned dataset is used as the input of seven machine-learning models based on fully connected Deep Neural Networks (DNN). The average error of ML models is between 1% to 3%, and the average error of the new predicted samples after the validation process is between 2% to 12%.
This research is mainly focused on the assessment of machine learning algorithms in the prediction of daylight and visual comfort metrics in the early design stages. A dataset was primarily developed from 2880 simulations derived from Honeybee for Grasshopper. The simulations were done for a shoebox space with a one side window. The alternatives emerged from different physical features, including room dimensions, interior surfaces reflectance, window dimensions and orientations, number of windows, and shading states. 5 metrics were used for daylight evaluations, including UDI, sDA, mDA, ASE, a…
PROCESS-2: A Benchmark Speech Corpus for Early Cognitive Impairment Detection
School of Computer Science, University of Sheffield（谢菲尔德大学计算机科学学院）；Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield（谢菲尔德转化神经科学研究所）
The acoustic environment can degrade speech quality during communication (e.g., video call, remote presentation, outside voice recording), and its impact is often unknown. Objective metrics for speech quality have proven challenging to develop given the multi-dimensionality of factors that affect speech quality and the difficulty of collecting labeled data. Hypothesizing the impact of acoustics on speech quality, this paper presents MOSRA: a non-intrusive multi-dimensional speech quality metric that can predict room acoustics parameters (SNR, STI, T60, DRR, and C50) alongside the overall mean …
Beyond Decodability: Do Acoustic Factors Drive Predictions in Speech-Based Alzheimer's Assessment?
Hertie Institute for AI in Brain Health（赫尔蒂人工智能脑健康研究所）；Tübingen AI Center（图宾根人工智能中心）；Sony Group Corporation（索尼集团公司）；Friedrich-Alexander-University Erlangen-Nürnberg（弗里德里希-亚历山大大学埃尔朗根-纽伦堡）
Traditionally, the quality of acoustic echo cancellers is evaluated using intrusive speech quality assessment measures such as ERLE \cite{g168} and PESQ \cite{p862}, or by carrying out subjective laboratory tests. Unfortunately, the former are not well correlated with human subjective measures, while the latter are time and resource consuming to carry out. We provide a new tool for speech quality assessment for echo impairment which can be used to evaluate the performance of acoustic echo cancellers. More precisely, we develop a neural network model to evaluate call quality degradations in two…
A Cross-Lingual Acoustic Disease-Alignment Framework for Respiratory Health Assessment from Spontaneous Speech
University of Maryland, College Park（马里兰大学学院公园分校）；Sylhet MAG Osmani Medical College Hospital（锡尔赫特MAG奥斯马尼医学院医院）；Dr. M R Khan Shishu Hospital & Institute of Child Health（M R 汗儿童医院与儿童健康研究所）；Line Reflection Ltd.（Line Reflection 有限公司）
Modality.ai, Inc.（Modality.ai公司）；University of California, San Francisco（加州大学旧金山分校）；EverythingALS, Peter Cohen Foundation（EverythingALS, Peter Cohen基金会）；Pr3vent, Inc.（Pr3vent公司）；IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）；Massachusetts Institute of Technology（麻省理工学院）；MGH Institute Health Professions（MGH健康职业学院）；Harvard University（哈佛大学）；University of Nebraska（内布拉斯加大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
We propose a cloud-based multimodal dialog platform for the remote assessment and monitoring of Amyotrophic Lateral Sclerosis (ALS) at scale. This paper presents our vision, technology setup, and an initial investigation of the efficacy of the various acoustic and visual speech metrics automatically extracted by the platform. 82 healthy controls and 54 people with ALS (pALS) were instructed to interact with the platform and completed a battery of speaking tasks designed to probe the acoustic, articulatory, phonatory, and respiratory aspects of their speech. We find that multiple acoustic (rate…
An Effective Strategy for Modeling Score Ordinality and Non-uniform Intervals in Automated Speaking Assessment
Department of Computer Science and Information Engineering, National Taiwan Normal University（国立台湾师范大学计算机科学与信息工程系）；Department of Educational Psychology and Counseling, National Taiwan Normal University（国立台湾师范大学教育心理学与辅导系）
Early and Late Time Acoustic Measures for Underwater Seismic Airgun Signals In Long-Term Acoustic Data Sets
Bioacoustics Research Program, Cornell Lab of Ornithology, Cornell University（康奈尔大学鸟类学实验室生物声学研究项目）；Applied Physics Laboratory, University of Washington（华盛顿大学应用物理实验室）
Bulbar ALS Detection Based on Analysis of Voice Perturbation and Vibrato
Belarusian State University of Informatics and Radioelectronics（白俄罗斯国立信息与无线电电子大学）；Republican Research and Clinical Center of Neurology and Neurosurgery（共和国神经学与神经外科研究临床中心）
Belarusian State University of Informatics and Radioelectronics（白俄罗斯国立信息与无线电电子大学）；Republican Research and Clinical Center of Neurology and Neurosurgery（共和国神经学与神经外科研究临床中心）
Amyotrophic lateral sclerosis (ALS) is incurable neurological disorder with rapidly progressive course. Common early symptoms of ALS are difficulty in swallowing and speech. However, early acoustic manifestation of speech and voice symptoms is very variable, that making their detection very challenging, both by human specialists and automatic systems. This study presents an approach to voice assessment for automatic system that separates healthy people from patients with ALS. In particular, this work focus on analysing of sustain phonation of vowels /a/ and /i/ to perform automatic classificat…
Zonguldak Bulent Ecevit University（宗古尔达克比伦特埃杰维特大学）；Badji Mokhtar Annaba University（安纳巴巴吉莫赫塔尔大学）
The new coronavirus disease (COVID-19) has been declared a pandemic since March 2020 by the World Health Organization. It consists of an emerging viral infection with respiratory tropism that could develop atypical pneumonia. Experts emphasize the importance of early detection of those who have the COVID-19 virus. In this way, patients will be isolated from other people and the spread of the virus can be prevented. For this reason, it has become an area of interest to develop early diagnosis and detection methods to ensure a rapid treatment process and prevent the virus from spreading. Since t…
Faculty of Electrical Engineering and Information Technologies, CMUS（斯科普里西里尔和迪乌斯大学电气工程与信息技术学院）；University College London（伦敦大学学院）；TU Dresden（德累斯顿工业大学）
The way infants use auditory cues to learn to speak despite the acoustic mismatch of their vocal apparatus is a hot topic of scientific debate. The simulation of early vocal learning using articulatory speech synthesis offers a way towards gaining a deeper understanding of this process. One of the crucial parameters in these simulations is the choice of features and a metric to evaluate the acoustic error between the synthesised sound and the reference target. We contribute with evaluating the performance of a set of 40 feature-metric combinations for the task of optimising the production of s…
Robust Generative Audio Quality Assessment: Disentangling Quality from Spurious Correlations
Dept. Computer Science and Information Engineering, National Taiwan Normal University, Taiwan（资讯工程系，国立台湾师范大学，台湾）；Institute of Computer Science, Academia Sinica, Taiwan（2 中国学术院信息科学研究所，台湾）；E.SUN Financial Holding Co., Ltd., Taiwan（3 E.SUN财务控股公司，台湾）；United Link Co., Ltd., Taiwan（4 联络科技有限公司，台湾）
In industrial applications, the early detection of malfunctioning factory machinery is crucial. In this paper, we consider acoustic malfunction detection via transfer learning. Contrary to the majority of current approaches which are based on deep autoencoders, we propose to extract features using neural networks that were pretrained on the task of image classification. We then use these features to train a variety of anomaly detection models and show that this improves results compared to convolutional autoencoders in recordings of four different factory machines in noisy environments. Moreov…
This paper describes the design and development of CUCHILD, a large-scale Cantonese corpus of child speech. The corpus contains spoken words collected from 1,986 child speakers aged from 3 to 6 years old. The speech materials include 130 words of 1 to 4 syllables in length. The speakers cover both typically developing (TD) children and children with speech disorder. The intended use of the corpus is to support scientific and clinical research, as well as technology development related to child speech assessment. The design of the corpus, including selection of words, participants recruitment, …
Effectiveness of IoT and Deep Learning for Detection and Severity Assessment of Postelectrotermes militaris in Tea Plantations
Sri Lanka Institute of Information Technology（斯里兰卡信息技术学院）；School of Molecular and Life Sciences, Curtin University（科廷大学分子与生命科学学院）；University of Kelaniya（凯拉尼亚大学）；Tea Research Institute of Sri Lanka（斯里兰卡茶叶研究所）；Murdoch University（默多克大学）
