Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

An online robot collision detection and identification scheme by supervised learning and Bayesian decision theory

This article is dedicated to developing an online collision detection and identification (CDI) scheme for human-collaborative robots. The scheme is composed of a signal classifier and an online diagnosor, which monitors the sensory signals of the robot system, detects the occurrence of a physical human-robot interaction, and identifies its type within a short period. In the beginning, we conduct an experiment to construct a data set that contains the segmented physical interaction signals with ground truth. Then, we develop the signal classifier on the data set with the paradigm of supervised learning. To adapt the classifier to the online application with requirements on response time, an auxiliary online diagnosor is designed using the Bayesian decision theory. The diagnosor provides not only a collision identification result but also a confidence index which represents the reliability of the result. Compared to the previous works, the proposed scheme ensures rapid and accurate CDI even in the early stage of a physical interaction. As a result, safety mechanisms can be triggered before further injuries are caused, which is quite valuable and important toward a safe human-robot collaboration. In the end, the proposed scheme is validated on a robot manipulator and applied to a demonstration task with collision reaction strategies. The experimental results reveal that the collisions are detected and classified within 20 ms with an overall accuracy of 99.6%, which confirms the applicability of the scheme to collaborative robots in practice

Audio Environment Design Applied To Long Duration Space Missions

In future space missions when distances and durations become longer, elements of space craft design, previously thought to be irrelevant, will carry extreme importance. Issues that may occur during a long term space mission are identified using research from analogue earth environments; locations on earth which have similar attributes to space, such as extreme climate or confinement. Music and sound research is than applied to these locales to show the positive changes that can result from the use of proper audio design, both in acoustics and generated sounds. Potential solutions are provided, as well as equipment options

A Discriminative Analysis of Fine-Grained Semantic Relations including Presupposition: Annotation and Classification

In contrast to classical lexical semantic relations between verbs, such as antonymy, synonymy or hypernymy, presupposition is a lexically triggered semantic relation that is not well covered in existing lexical resources. It is also understudied in the field of corpus-based methods of learning semantic relations. Yet, presupposition is very important for semantic and discourse analysis tasks, given the implicit information that it conveys. In this paper we present a corpus-based method for acquiring presupposition-triggering verbs along with verbal relata that express their presupposed meaning. We approach this difficult task using a discriminative classification method that jointly determines and distinguishes a broader set of inferential semantic relations between verbs. The present paper focuses on important methodological aspects of our work: (i) a discriminative analysis of the semantic properties of the chosen set of relations, (ii) the selection of features for corpus-based classification and (iii) design decisions for the manual annotation of fine-grained semantic relations between verbs. (iv) We present the results of a practical annotation effort leading to a gold standard resource for our relation inventory, and (v) we report results for automatic classification of our target set of fine-grained semantic relations, including presupposition. We achieve a classification performance of 55% F1-score, a 100% improvement over a best-feature baseline

The deep space network

Network engineering, hardware and software development, and tracking station operations for support of deep space unmanned flight projects are summarized

Deep sleep: deep learning methods for the acoustic analysis of sleep-disordered breathing

Sleep-disordered breathing (SDB) is a serious and prevalent condition that results from the collapse of the upper airway during sleep, which leads to oxygen desaturations, unphysiological variations in intrathoracic pressure, and sleep fragmentation. Its most common form is obstructive sleep apnoea (OSA). This has a big impact on quality of life, and is associated with cardiovascular morbidity. Polysomnography, the gold standard for diagnosing SDB, is obtrusive, time-consuming and expensive. Alternative diagnostic approaches have been proposed to overcome its limitations. In particular, acoustic analysis of sleep breathing sounds offers an unobtrusive and inexpensive means to screen for SDB, since it displays symptoms with unique acoustic characteristics. These include snoring, loud gasps, chokes, and absence of breathing. This thesis investigates deep learning methods, which have revolutionised speech and audio technology, to robustly screen for SDB in typical sleep conditions using acoustics. To begin with, the desirable characteristics for an acoustic corpus of SDB, and the acoustic definition of snoring are considered to create corpora for this study. Then three approaches are developed to tackle increasingly complex scenarios. Firstly, with the aim of leveraging a large amount of unlabelled SDB data, unsupervised learning is applied to learn novel feature representations with deep neural networks for the classification of SDB events such as snoring. The incorporation of contextual information to assist the classifier in producing realistic event durations is investigated. Secondly, the temporal pattern of sleep breathing sounds is exploited using convolutional neural networks to screen participants sleeping by themselves for OSA. The integration of acoustic features with physiological data for screening is examined. Thirdly, for the purpose of achieving robustness to bed partner breathing sounds, recurrent neural networks are used to screen a subject and their bed partner for SDB in the same session. Experiments conducted on the constructed corpora show that the developed systems accurately classify SDB events, screen for OSA with high sensitivity and specificity, and screen a subject and their bed partner for SDB with encouraging performance. In conclusion, this thesis makes promising progress in improving access to SDB diagnosis through low-cost and non-invasive methods

Relative Use of Phonaesthemes in the Constitution and Development of Genres

My research question is Does the presence of phonaesthemes in words play a role in the constitution and evolution of genres? A phonaestheme is a phonemic grouping that correlates well above chance with a particular semantic quality in etymologically unrelated words; phonaesthematic words are generally seen as vivid, expressive, and involved. I explore the nature of phonaesthemes and genres and the role of features such as phonaesthemes in the constitution of genres. I select a set of phonaesthemes to evaluate and choose a representative set of lemmas and matching non-phonaesthematic lemmas. I survey these in six genres over three time periods in the US and the UK. I analyze the results and their implications for phonaesthemes and for genre constitution, finding, among other things, that phonaesthemes are important in the social positioning of genres. The summary answer to my research question is thus found to be Yes, it does

The 2nd International Electronic Conference on Applied Sciences

This book is focused on the works presented at the 2nd International Electronic Conference on Applied Sciences, organized by Applied Sciences from 15 to 31 October 2021 on the MDPI Sciforum platform. Two decades have passed since the start of the 21st century. The development of sciences and technologies is growing ever faster today than in the previous century. The field of science is expanding, and the structure of science is becoming ever richer. Because of this expansion and fine structure growth, researchers may lose themselves in the deep forest of the ever-increasing frontiers and sub-fields being created. This international conference on the Applied Sciences was started to help scientists conduct their own research into the growth of these frontiers by breaking down barriers and connecting the many sub-fields to cut through this vast forest. These functions will allow researchers to see these frontiers and their surrounding (or quite distant) fields and sub-fields, and give them the opportunity to incubate and develop their knowledge even further with the aid of this multi-dimensional network