Towards a Cross-Disciplinary Sound Design Methodology: A Focus on Semiotics and Linguistics

Esta tese foca-se no mundo do design de som, centrando-se no desenvolvimento de uma metodologia que engloba várias disciplinas. Explora o papel do design de som na transmissão de mensagens, servindo de interface entre utilizadores e dispositivos. O estudo também investiga os paralelos entre a semiótica e a linguística e o design de som, interpretando como os sons podem atuar como sinais que representam outras entidades, com base em convenções sociais estabelecidas, e como podemos utilizar a linguística como modelo para criar sons não-falados que, tal como a linguagem, transmitem sistematicamente um significado ao utilizador. Através desta análise abrangente, o trabalho pretende contribuir para o campo, propondo uma metodologia interdisciplinar para o design de som, melhorando assim a experiência auditiva em diversos contextos.This thesis delves into the intricate world of sound design, focusing on the development of a methodology that encompasses various disciplines. It explores the role of sound design in conveying messages, serving as an interface between users and devices. The study also investigates the parallels between semiotics and linguistics and sound design, interpreting how sounds can act as signs that represent other entities, based on established social conventions and how we can use linguistics as a model to create non-speech sounds that just like language systematically convey meaning to the user. Through this comprehensive analysis, the work aims to contribute to the field by proposing a cross-disciplinary methodology for sound design, thereby enhancing the auditory experience in diverse contexts

NMF-Based Spectral Analysis for Acoustic Event Classification Tasks

Proceedings of: 6th International Conference The Non-Linear Speech Processing (NOLISP 2013). Mons, Belgium, June 19-21, 2013.In this paper, we propose a new front-end for Acoustic Event Classification tasks (AEC). First, we study the spectral contents of different acoustic events by applying Non-Negative Matrix Factorization (NMF) on their spectral magnitude and compare them with the structure of speech spectra. Second, from the findings of this study, we propose a new parameterization for AEC, which is an extension of the conventional Mel Frequency Cepstrum Coefficients (MFCC) and is based on the high pass filtering of acoustic event spectra. Also, the influence of different frequency scales on the classification rate of the whole system is studied. The evaluation of the proposed features for AEC shows that relative error reductions about 12% at segment level and about 11% at target event level with respect to the conventional MFCC are achieved.This work has been partially supported by the Spanish Government grants TSI-020110-2009-103, IPT-120000-2010-24 and TEC2011-26807. Financial support from the Fundaci´on Carolina and Universidad Católica San Pablo, Arequipa.Publicad

Machine Learning Methods with Noisy, Incomplete or Small Datasets

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios

An architecture to predict anomalies in industrial processes

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Internet of Things (IoT) and machine learning algorithms (ML) are enabling a revolutionary change in digitization in numerous areas, benefiting Industry 4.0 in particular. Predictive maintenance using machine learning models is being used to protect assets in industry. In this paper, an architecture for predicting anomalies in industrial processes was proposed in which SMEs can be guided in implementing an IIoT architecture for predictive maintenance (PdM). This research was conducted to understand what machine learning architectures and models are generally used by industry for PdM. An overview of the concepts of the Industrial Internet of Things (IIoT), machine learning (ML), and predictive maintenance (PdM) was provided, and through a systematic literature review, it was possible to understand their applications and which technologies enable their use. The survey revealed that PdM applications are increasingly common and that there are many studies on the development of new ML techniques. The survey conducted confirmed the usefulness of the artifact and showed the need for an architecture to guide the implementation of PdM. This research can be a contribution for SMEs, allowing them to become more efficient and reduce both production and maintenance costs in order to keep up with multinational companies

The role of posttraumatic stress disorder in explaining the psychosocial outcome of subarachnoid haemorrhage patients and their informal carers in both the short- and long-term

Surviving subarachnoid haemorrhage (SAH) patients' experience significantly reduced health-related quality of life (HRQoL) in both the short- and long-term, as well as mysterious symptoms of fatigue and sleep dysfunction. Patients’ family members and friends - who often act as their informal carers - can also experience psychosocial disability. The cause for these poor outcomes remains unknown. Traditional explanations focusing on the neurological sequelae associated with SAH or the characteristics of the illness are not satisfactory; nor are attempts to explain family members' difficulties on the basis of carer burden. The hypothesis which is tested in this thesis is that post-traumatic stress disorder (PTSD) may be abnormally high in both the SAH patient and 'significant other' (SO) population and that this may explain their outcomes. SAH patients are known to be at risk of suffering from PTSD, but it is unknown if this explains their outcome. In terms of patients' SOs, they are known to experience psychiatric symptoms and I suggest these could be caused by their development of PTSD, but this has never been examined. In Part One (Chapter 2-5), I focus on patients' outcomes. Before examining my PTSD hypothesis, I present a meta-analysis (Chapter 2) I conducted of studies which have tried to explain patients' outcome using neurological factors. I conducted the meta-analysis as a tendency for prior studies to be underpowered and use unreliable statistics could have meant that the actual importance of traditional factors was obscured. The results of my meta-analysis however did not support this possibility and instead showed traditional neurological variables did not explain patients' outcome. With this in mind, I then present a longitudinal study (Chapter 5) in which I examined one of the largest prospective series of SAH patients to establish PTS D's explanative importance. Using regression analyses, this study showed PTSD was the best predictor for patients' mental HRQoL - the domain most persistently impaired. It also helped predict patients' physical HRQoL. Moreover, PTSD was linked to sleep problems and may therefore cause fatigue. Crucially, to establish the cause of PTSD, logistic regression was performed. This showed that maladaptive stress coping strategies were the best predictor for PTSD development. In Part Two of the thesis (Chapter 6), I present my longitudinal study of one of the largest prospective samples of SOs. All SOs were assessed with a diagnostic PTSD measure and coping skills were assessed. An elevated incidence of PTSD was found in both the short- and long-term. Although SOs' PTSD did not impinge on the recovery of the SAH patients being cared for, given that it is important to ensure SOs continue caring, regression results are presented which show the cause of SOs' PTSD was (at least in the short- term) due to the use of maladaptive coping strategies. The overarching conclusion is that the elevated incidence of PTSD in SAH patients and SOs helps explain why they experience psychosocial disability. In the final part of the thesis (Chapter 8) the clinical and theoretical implications of this conclusion are considered, such as that teaching patients and their SOs more effective coping skills might prevent PTSD and psychosocial disability

High-speed civil transport flight- and propulsion-control technological issues

Technology advances required in the flight and propulsion control system disciplines to develop a high speed civil transport (HSCT) are identified. The mission and requirements of the transport and major flight and propulsion control technology issues are discussed. Each issue is ranked and, for each issue, a plan for technology readiness is given. Certain features are unique and dominate control system design. These features include the high temperature environment, large flexible aircraft, control-configured empennage, minimizing control margins, and high availability and excellent maintainability. The failure to resolve most high-priority issues can prevent the transport from achieving its goals. The flow-time for hardware may require stimulus, since market forces may be insufficient to ensure timely production. Flight and propulsion control technology will contribute to takeoff gross weight reduction. Similar technology advances are necessary also to ensure flight safety for the transport. The certification basis of the HSCT must be negotiated between airplane manufacturers and government regulators. Efficient, quality design of the transport will require an integrated set of design tools that support the entire engineering design team