376 research outputs found
Mobile heritage practices. Implications for scholarly research, user experience design, and evaluation methods using mobile apps.
Mobile heritage apps have become one of the most popular means for audience
engagement and curation of museum collections and heritage contexts. This
raises practical and ethical questions for both researchers and practitioners, such
as: what kind of audience engagement can be built using mobile apps? what are
the current approaches? how can audience engagement with these experience
be evaluated? how can those experiences be made more resilient, and in turn
sustainable? In this thesis I explore experience design scholarships together with
personal professional insights to analyse digital heritage practices with a view to
accelerating thinking about and critique of mobile apps in particular. As a result,
the chapters that follow here look at the evolution of digital heritage practices,
examining the cultural, societal, and technological contexts in which mobile
heritage apps are developed by the creative media industry, the academic
institutions, and how these forces are shaping the user experience design
methods. Drawing from studies in digital (critical) heritage, Human-Computer
Interaction (HCI), and design thinking, this thesis provides a critical analysis of
the development and use of mobile practices for the heritage. Furthermore,
through an empirical and embedded approach to research, the thesis also
presents auto-ethnographic case studies in order to show evidence that mobile
experiences conceptualised by more organic design approaches, can result in
more resilient and sustainable heritage practices. By doing so, this thesis
encourages a renewed understanding of the pivotal role of these practices in the
broader sociocultural, political and environmental changes.AHRC REAC
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Review of substitutive assistive tools and technologies for people with visual impairments: recent advancements and prospects
YesThe development of many tools and technologies for people with visual impairment has become a major priority in the
field of assistive technology research. However, many of these technology advancements have limitations in terms of the
human aspects of the user experience (e.g., usability, learnability, and time to user adaptation) as well as difficulties in
translating research prototypes into production. Also, there was no clear distinction between the assistive aids of adults
and children, as well as between āpartial impairmentā and ātotal blindnessā. As a result of these limitations, the produced
aids have not gained much popularity and the intended users are still hesitant to utilise them. This paper presents a comprehensive review of substitutive interventions that aid in adapting to vision loss, centred on laboratory research studies
to assess user-system interaction and system validation. Depending on the primary cueing feedback signal offered to the
user, these technology aids are categorized as visual, haptics, or auditory-based aids. The context of use, cueing feedback
signals, and participation of visually impaired people in the evaluation are all considered while discussing these aids.
Based on the findings, a set of recommendations is suggested to assist the scientific community in addressing persisting
challenges and restrictions faced by both the totally blind and partially sighted people
Recommended from our members
Sonic heritage: listening to the past
History is so often told through objects, images and photographs, but the potential of sounds to reveal place and space is often neglected. Our research project āSonic Palimpsestā1 explores the potential of sound to evoke impressions and new understandings of the past, to embrace the sonic as a tool to understand what was, in a way that can complement and add to our predominant visual understandings. Our work includes the expansion of the Oral History archives held at Chatham Dockyard to include womenās voices and experiences, and the creation of sonic works to engage the public with their heritage. Our research highlights the social and cultural value of oral history and field recordings in the transmission of knowledge to both researchers and the public. Together these recordings document how buildings and spaces within the dockyard were used and experienced by those who worked there. We can begin to understand the social and cultural roles of these buildings within the community, both past and present
A Framework for Site-Specific Spatial Audio Applications
As audio recording and reproduction technology has advanced over the past five decades, increasing attention has been paid to recreating the highly spatialised listening experience we understand from our physical environment. This is the logical next step in the quest for increasing audio clarity, particularly as virtual reality gaming and augmented reality experiences become more widespread.
This study sought to develop and demonstrate a technical framework for the production of site-specific audio-based works that is user-friendly and cost effective. The system was intended to be used by existing content producers and audio programmers to work collaboratively with a range of site-based organisations such as museums and galleries to produce an audio augmentation of the physicality of the space.
This research was guided by four key aims:
1. Demonstrate a compositional method for immersive spatial audio that references the novel physical environment and the listenerās movement within it.
2. Describe a framework for the development and deployment of a spatial audio visitor technology system.
3. Prototype a naturalistic method for the delivery and navigation of contextual information via audio.
4. Deploy, demonstrate, and evaluate a spatial audio experience within a representative environment.
The resulting system makes use of a range of existing technologies to provide a development experience and output that meets a clearly defined set of criteria.
Furthermore, a case study application has been developed that demonstrates the use of the system to augment a selection of six paintings in a gallery space. For each of these paintings, a creative spatial composition was produced that demonstrates the principles of spatial composition discussed in this thesis. A spoken informational layer sits on top of this acting as a museum audio guide, featuring navigation using head gestures for a hands-free experience. This thesis presents a detailed discussion of the artistic intentions and techniques employed in the production of the six soundscapes, as well as an evaluation of the resulting application in use in a public gallery space
Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends
Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9
International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications
Security and Privacy for Modern Wireless Communication Systems
The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in nodeāedgeācloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks
Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)
This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21ā22 September 2023
Voicing Kinship with Machines: Diffractive Empathetic Listening to Synthetic Voices in Performance.
This thesis contributes to the field of voice studies by analyzing the design and production of synthetic voices in performance. The work explores six case studies, consisting of different performative experiences of the last decade (2010- 2020) that featured synthetic voice design. It focusses on the political and social impact of synthetic voices, starting from yet challenging the concepts of voice in the machine and voice of the machine. The synthetic voices explored are often playing the role of simulated artificial intelligences, therefore this thesis expands its questions towards technology at large. The analysis of the case studies follows new materialist and posthumanist premises, yet it tries to confute the patriarchal and neoliberal approach towards technological development through feminist and de-colonial approaches, developing a taxonomy for synthetic voices in performance. Chapter 1 introduces terms and explains the taxonomy. Chapter 2 looks at familiar representations of fictional AI. Chapter 3 introduces headphone theatre exploring immersive practices. Chapters 4 and 5 engage with chatbots. Chapter 6 goes in depth exploring Human and Artificial Intelligence interaction, whereas chapter 7 moves slightly towards music production and live art. The body of the thesis includes the work of Pipeline Theatre, Rimini Protokoll, Annie Dorsen, BeguĢm Erciyas, and Holly Herndon. The analysis is informed by posthumanism, feminism, and performance studies, starting from my own practice as sound designer and singer, looking at aesthetics of reproduction, audience engagement, and voice composition. This thesis has been designed to inspire and provoke practitioners and scholars to explore synthetic voices further, question predominant biases of binarism and acknowledge their importance in redefining technology
The assessment and development of methods in (spatial) sound ecology
As vital ecosystems across the globe enter unchartered pressure from climate change industrial land use, understanding the processes driving ecosystem viability has never been more critical. Nuanced ecosystem understanding comes from well-collected field data and a wealth of associated interpretations. In recent years the most popular methods of ecosystem monitoring have revolutionised from often damaging and labour-intensive manual data collection to automated methods of data collection and analysis. Sound ecology describes the school of research that uses information transmitted through sound to infer properties about an area's species, biodiversity, and health. In this thesis, we explore and develop state-of-the-art automated monitoring with sound, specifically relating to data storage practice and spatial acoustic recording and data analysis.
In the first chapter, we explore the necessity and methods of ecosystem monitoring, focusing on acoustic monitoring, later exploring how and why sound is recorded and the current state-of-the-art in acoustic monitoring. Chapter one concludes with us setting out the aims and overall content of the following chapters. We begin the second chapter by exploring methods used to mitigate data storage expense, a widespread issue as automated methods quickly amass vast amounts of data which can be expensive and impractical to manage. Importantly I explain how these data management practices are often used without known consequence, something I then address. Specifically, I present evidence that the most used data reduction methods (namely compression and temporal subsetting) have a surprisingly small impact on the information content of recorded sound compared to the method of analysis. This work also adds to the increasing evidence that deep learning-based methods of environmental sound quantification are more powerful and robust to experimental variation than more traditional acoustic indices.
In the latter chapters, I focus on using multichannel acoustic recording for sound-source localisation. Knowing where a sound originated has a range of ecological uses, including counting individuals, locating threats, and monitoring habitat use. While an exciting application of acoustic technology, spatial acoustics has had minimal uptake owing to the expense, impracticality and inaccessibility of equipment. In my third chapter, I introduce MAARU (Multichannel Acoustic Autonomous Recording Unit), a low-cost, easy-to-use and accessible solution to this problem. I explain the software and hardware necessary for spatial recording and show how MAARU can be used to localise the direction of a sound to within Ā±10Ė accurately. In the fourth chapter, I explore how MAARU devices deployed in the field can be used for enhanced ecosystem monitoring by spatially clustering individuals by calling directions for more accurate abundance approximations and crude species-specific habitat usage monitoring. Most literature on spatial acoustics cites the need for many accurately synced recording devices over an area. This chapter provides the first evidence of advances made with just one recorder.
Finally, I conclude this thesis by restating my aims and discussing my success in achieving them. Specifically, in the thesisā conclusion, I reiterate the contributions made to the field as a direct result of this work and outline some possible development avenues.Open Acces
A Review of Deep Learning Techniques for Speech Processing
The field of speech processing has undergone a transformative shift with the
advent of deep learning. The use of multiple processing layers has enabled the
creation of models capable of extracting intricate features from speech data.
This development has paved the way for unparalleled advancements in speech
recognition, text-to-speech synthesis, automatic speech recognition, and
emotion recognition, propelling the performance of these tasks to unprecedented
heights. The power of deep learning techniques has opened up new avenues for
research and innovation in the field of speech processing, with far-reaching
implications for a range of industries and applications. This review paper
provides a comprehensive overview of the key deep learning models and their
applications in speech-processing tasks. We begin by tracing the evolution of
speech processing research, from early approaches, such as MFCC and HMM, to
more recent advances in deep learning architectures, such as CNNs, RNNs,
transformers, conformers, and diffusion models. We categorize the approaches
and compare their strengths and weaknesses for solving speech-processing tasks.
Furthermore, we extensively cover various speech-processing tasks, datasets,
and benchmarks used in the literature and describe how different deep-learning
networks have been utilized to tackle these tasks. Additionally, we discuss the
challenges and future directions of deep learning in speech processing,
including the need for more parameter-efficient, interpretable models and the
potential of deep learning for multimodal speech processing. By examining the
field's evolution, comparing and contrasting different approaches, and
highlighting future directions and challenges, we hope to inspire further
research in this exciting and rapidly advancing field
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