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Journal articleJones CM, Austin K, Augustus SN, et al., 2023,
An instrumented mouthguard for real-time measurement of head kinematics under a large range of sport specific accelerations
, Sensors, Vol: 23, Pages: 1-15, ISSN: 1424-8220BACKGROUND: Head impacts in sports can produce brain injuries. The accurate quantification of head kinematics through instrumented mouthguards (iMG) can help identify underlying brain motion during injurious impacts. The aim of the current study is to assess the validity of an iMG across a large range of linear and rotational accelerations to allow for on-field head impact monitoring. METHODS: Drop tests of an instrumented helmeted anthropometric testing device (ATD) were performed across a range of impact magnitudes and locations, with iMG measures collected concurrently. ATD and iMG kinematics were also fed forward to high-fidelity brain models to predict maximal principal strain. RESULTS: The impacts produced a wide range of head kinematics (16-171 g, 1330-10,164 rad/s2 and 11.3-41.5 rad/s) and durations (6-18 ms), representing impacts in rugby and boxing. Comparison of the peak values across ATD and iMG indicated high levels of agreement, with a total concordance correlation coefficient of 0.97 for peak impact kinematics and 0.97 for predicted brain strain. We also found good agreement between iMG and ATD measured time-series kinematic data, with the highest normalized root mean squared error for rotational velocity (5.47 ± 2.61%) and the lowest for rotational acceleration (1.24 ± 0.86%). Our results confirm that the iMG can reliably measure laboratory-based head kinematics under a large range of accelerations and is suitable for future on-field validity assessments.
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Journal articleKench S, Squires I, Cooper S, 2023,
TauFactor 2: A GPU accelerated python tool formicrostructural analysis
, Journal of Open Source Software, Vol: 8, Pages: 5358-5358 -
Journal articleAttar HR, Foster A, Li N, 2023,
Development of a deep learning platform for sheet stamping geometry optimisation under manufacturing constraints
, Engineering Applications of Artificial Intelligence, Vol: 123, Pages: 1-23, ISSN: 0952-1976Sheet stamping is a widely adopted manufacturing technique for producing complex structural components with high stiffness-to-weight ratios. However, designing such components is a non-trivial task that requires careful consideration of manufacturing constraints to avoid introducing defects in the final product. To address this challenge, this research introduces a novel deep-learning-based platform that optimises 3D component designs by considering manufacturing capabilities. This platform was realised by developing a methodology to combine two neural networks that handle non-parametric geometry representations, namely a geometry generator based on Signed Distance Fields (SDFs) and an image-based manufacturability surrogate model. This combination enables the optimisation of complex geometries that can be represented using various parameterisation schemes. The optimisation approach implemented in the platform utilises gradient-based techniques to update the inputs to the geometry generator based on manufacturability information from the surrogate model. The platform is demonstrated using two geometry classes, Corners and Bulkheads, each having three geometry subclasses, with four diverse case studies conducted to optimise these geometries under post-stamped thinning constraints. The case studies demonstrate how the platform enables free morphing of complex geometries, while also guiding manufacturability-driven geometric changes in a direction that leads to significant improvements in component quality. For instance, one of the cases shows that optimising the complex component geometry can reduce the maximum thinning from 45% to satisfy the thinning constraint of 10%. By utilising the proposed platform, designers can identify optimal component geometries that ensure manufacturing feasibility for sheet stamping, reducing design development time and design costs.
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Journal articleLee JJ, Mohammed AA, Pullen A, et al., 2023,
Mechanical characterisation of 3D printed lightweight lattice structures with varying internal design alterations
, MATERIALS TODAY COMMUNICATIONS, Vol: 36 -
Journal articleHarrison J, Lucas A, Cunningham J, et al., 2023,
Exploring the opportunities of haptic technology in the practice of visually impaired and blind sound creatives
, Arts, Vol: 12, ISSN: 2076-0752Visually impaired and blind (VIB) people as a community face several access barriers when using technology. For users of specialist technology, such as digital audio workstations (DAWs), these access barriers become increasingly complex—often stemming from a vision-centric approach to user interface design. Haptic technologies may present opportunities to leverage the sense of touch to address these access barriers. In this article, we describe a participant study involving interviews with twenty VIB sound creatives who work with DAWs. Through a combination of semi-structured interviews and a thematic analysis of the interview data, we identify key issues relating to haptic audio and accessibility from the perspective of VIB sound creatives. We introduce the technical and practical barriers that VIB sound creatives encounter, which haptic technology may be capable of addressing. We also discuss the social and cultural aspects contributing to VIB people’s uptake of new technology and access to the music technology industry.
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Journal articleAttar HR, Foster A, Li N, 2023,
Implicit neural representations of sheet stamping geometries with small-scale features
, Engineering Applications of Artificial Intelligence, Vol: 123, Pages: 1-21, ISSN: 0952-1976Geometric deep learning models, like Convolutional Neural Networks (CNNs), show promise as surrogate models for predicting sheet stamping manufacturability but lack design variables essential for inverse problems like geometric optimisation. Recent developments in deep learning have enabled geometry generation from compact latent spaces that are suitable for optimisation. However, current methods do not accurately model small-scale geometric features that are crucial for stamping performance. This study proposes a new deep learning-based method to address this limitation and generate detailed stamping geometries for optimisation. Specifically, neural networks are trained to generate Signed Distance Fields (SDFs) for stamping geometries, where the zero-level-set of each SDF implicitly represents the generated geometry. A new training approach is proposed for generating SDFs of stamping geometries, which involves supervising geometric properties of the SDFs. A novel loss function is introduced that directly acts on the zero-level-set and places high emphasis on learning small-scale features. This approach is compared with the state-of-the-art approach DeepSDF by Park et al. (2019), which explicitly supervises SDF values using ground truth data. The geometry generation performance of networks trained using both approaches is evaluated quantitatively and qualitatively. The results demonstrate significantly greater geometric accuracy with the proposed approach, which can faithfully generate small-scale features. Further analysis of the new approach reveals an organised learned latent space and varying the network input generates high-quality geometries from this space. By integrating with CNN-based manufacturability surrogate models by Attar et al. (2021), this work could enable the first-ever manufacturability-constrained optimisation of arbitrary sheet stamping geometries, potentially reducing geometry design time and cost.
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Journal articleLiu Y, Xu M, Zhao Y, et al., 2023,
Multi-doped ceria-based composite as a promising low-temperature electrolyte with enhanced ionic conductivity for steam electrolysis
, MOLECULAR SYSTEMS DESIGN & ENGINEERING, Vol: 8, Pages: 992-1003, ISSN: 2058-9689 -
Conference paperEspinoza Lau-Choleon F, Cook D, Butler C, et al., 2023,
Supporting dementia caregivers in Peru through chatbots: generative AI vs structured conversations
, 36th International BCS Human-Computer Interaction Conference 36th International BCS Human-Computer Interaction Conference Human-Computer Interaction Conference, Publisher: Association for Computing Machinery (ACM)In Peru, dementia caregivers face burnout, depression, stress, and financial strain. Addressing their needs involves tackling the intricacies of caregiving and managing emotional burdens. Chatbots can serve as a viable support mechanism in regions with limited resources. This study delves into the perceptions of dementia caregivers in Peru regarding a chatbot tailored to offer care navigation andemotional support. We divided the study into three phases: the initial stage encompassed engaging stakeholders to define design requirements for the chatbot; the second stage focused on the creation of ‘Ana’, a chatbot for dementia caregivers; and the final stage assessed the chatbot through interviews and a caregiver satisfaction survey. ‘Ana’ was tested in two configurations - oneemployed pre-defined conversation patterns, while the other harnessed generative AI for more dynamic responses. The findings reveal that caregivers seek immediate access to information on handling behavioural symptoms and a platform for emotional release. Moreover, participantspreferred the generative AI alternative of Ana, as it was perceived to be more empathic and human-like. The participants valued the generative approach despite knowing the potential risk of receiving inaccurate information.
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Conference paperWidjaya MA, Bermudez J, Moradbakhti L, et al., 2023,
Drivers of trust in generative AI-powered voice assistants: the role of references
, 36th International BCS Human-Computer Interaction ConferenceThe boom in generative artificial intelligence (AI) and continuing growth of Voice Assistants (VAs) suggests their trajectories will converge. This conjecture aligns with the development of AI-driven conversational agents, aiming to utilise advance natural language processing (NLP) methods to enhance the capabilities of voice assistants. However, design guidelines for VAs prioritise maximum efficiency by advocating for the use of concise answers. This poses a conflict with the challenges around generative AI, such as inaccuracies and misinterpretation, as shorter responses may not adequately provide users with meaningful information. AI-VA systems can adapt drivers of trust formation, such as references and authorship, to improve credibility. A better understanding of user behaviour when using the system is needed to develop revised design recommendations for AI-powered VA systems. This paper reports an online survey of 256 participants residing in the U.K. and nine follow-up interviews, where user behaviour is investigated to identify drivers of trust in the context of obtaining digital information from a generative AI-based VA system. Adding references is promising as a tool for increasing trust in systems producing text, yet we found no evidence that the inclusion of references in a VA response contributed towards the perceived reliability or trust towards the system. We examine further variables driving user trust in AI-powered VA systems.
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Conference paperMartin V, Picinali L, 2023,
Comparing online vs. lab-based experimental approaches for the perceptual evaluation of artificial reverberation
, Forum Acusticum 2023A common approach for reproducing room acoustics effects is geometrical acoustics. The accuracy of such anapproach is tied, among other variables, to the geometrical accuracy of the simulated room, and to the information regarding the absorption coefficients of its materials.However, from a perceptual standpoint, a model that accounts for all of a room’s features would come at a highcomputational cost and could be redundant. As a result, acompromise can be reached between the perceived quality (e.g. authenticity, immersion, etc.) of the replicatedroom effect and the model’s complexity. The purpose ofthis study is to look into the perceptual impact of simplifying the room geometry and minimizing the numberof materials’ absorption coefficients. Two separate experiments were conducted, both based on the MUSHRAmethodology: one was run in a controlled lab environment through a Virtual Reality (VR) headset, while theother was run through a web-based interface. This paperfocuses on the differences between the two protocols’ impact on the results. It appears that the online-based experiment, notwithstanding the lack of control of the playback system and environment, and the participants’ likely limited attention, produced minor but substantial differences with the results of the VR experiment.
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Conference paperDaugintis R, Barumerli R, Geronazzo M, et al., 2023,
Initial evaluation of an auditory-model-aided selection procedure for non-individual HRTFs
, Forum Acusticum, Pages: 1-8, ISSN: 2221-3767Binaural spatial audio reproduction systems use measuredor simulated head-related transfer functions (HRTFs),which encode the effects of the outer ear and body onthe incoming sound to recreate a realistic spatial auditoryfield around the listener. The sound localisation cues embedded in the HRTF are highly personal. Establishingperceptual similarity between different HRTFs in a reliable manner is challenging due to a combination of acoustic and non-acoustic aspects affecting our spatial auditoryperception. To account for these factors, we propose anautomated procedure to select the ‘best’ non-individualHRTF dataset from a pool of measured ones. For a groupof human participants with their own acoustically measured HRTFs, a multi-feature Bayesian auditory sound localisation model is used to predict individual localisationperformance with the other HRTFs from within the group.Then, the model selection of the ‘best’ and the ‘worst’non-individual HRTFs is evaluated via an actual localisation test and a subjective audio quality assessment in comparison with individual HRTFs. A successful model-aidedobjective selection of the ‘best’ non-individual HRTF mayprovide relevant insights for effective and handy binaural spatial audio solutions in virtual/augmented reality(VR/AR) applications.
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Conference paperSadek M, Calvo RA, Mougenot C, 2023,
Trends, challenges and processes in conversational agent design: exploring practitioners’ views through semi-structured interviews
, CUI '23: ACM conference on Conversational User Interfaces, Publisher: ACM, Pages: 1-10The aim of this study is to explore the challenges and experiences of conversational agent (CA) practitioners in order to highlight their practical needs and bring them into consideration within the scholarly sphere. A range of data scientists, conversational designers, executive managers and researchers shared their opinions and experiences through semi-structured interviews. They were asked about emerging trends, the challenges they face, and the design processes they follow when creating CAs. In terms of trends, findings included mixed feelings regarding no-code solutions and a desire for a separation of roles. The challenges mentioned included a lack of socio-technical tools and conversational archetypes. Finally, practitioners followed different design processes and did not use the design processes described in the academic literature. These findings were analyzed to establish links between practitioners’ insights and discussions in related literature. The goal of this analysis is to highlight research-practice gaps by synthesising five practitioner needs that are not currently being met. By highlighting these research-practice gaps and foregrounding the challenges and experiences of CA practitioners, we can begin to understand the extent to which emerging literature is influencing industrial settings and where more research is needed to better support CA practitioners in their work.
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Journal articleSadan MK, Song E, Yu H, et al., 2023,
Extended cycling performance of micron-sized bismuth anodes for lithium-ion batteries: self-healing of an alloy-type anode for lithium batteries
, JOURNAL OF MATERIALS CHEMISTRY A, Vol: 11, Pages: 15466-15474, ISSN: 2050-7488 -
Conference paperSmith F, Sadek M, Mougenot C, 2023,
Empowering end-users in co-designing AI: an AI literacy card-based toolkit for non-technical audiences
, 36th International BCS Human-Computer Interaction Conference -
Journal articleSun S, Wang J, Zong S, et al., 2023,
Integration Plasma Strategy Controlled Interfacial Chemistry Regulation Enabling Planar Lithium Growth in Solid-State Lithium Metal Batteries
, ADVANCED FUNCTIONAL MATERIALS, ISSN: 1616-301X -
Conference paperZhou H, Yang S, Halamek L, et al., 2023,
A method to use haptic feedback of laryngoscope force vector for endotracheal intubation training
, IEEE International Conference on Robotics and Automation : ICRA, Publisher: IEEE, Pages: 6810-6816, ISSN: 2152-4092Endotracheal intubation is a mandatory competency for most medical staff. This procedure involves opening the entrance of the patient's upper windpipe using a laryngoscope and then inserting a tube into the windpipe to supply Oxygen to the patient. This time critical intervention requires careful control of the force vector on the tongue to lift it parallel to the jaw than to push the jaw to open the mouth. However, traditional intubation training methods in which novices practice intubation on prostheses lack haptic feedback to improve force control. We designed a sensorised intubation training phantom that can provide trainees with vibrotactile feedback reflecting the laryngoscope's force on the tongue. The critical component of this phantom is a silicon rubber tongue embedded with magnets and hall effect sensors. We calibrated the hall effect sensor readings to predict the force vector exerted on the tongue with errors less than 0.5 N in the lifting and pushing directions. We conducted a controlled experiment, mainly comparing the training results between participants with and without haptic feedback. Results show a statistically significant drop in the undesired forces due to haptic feedback, and the skill is retained when tested after 24 hours without haptic feedback.
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Conference paperStedman H, Kocer BB, van Zalk N, et al., 2023,
Evaluating immersive teleoperation interfaces: coordinating robot radiation monitoring tasks in nuclear facilities
, 2023 IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 11972-11978We present a virtual reality (VR) teleoperation interface for a ground-based robot, featuring dense 3D environment reconstruction and a low latency video stream, with which operators can immersively explore remote environments. At the UK Atomic Energy Authority's (UKAEA) Remote Applications in Challenging Environments (RACE) facility, we applied the interface in a user study where trained robotics operators completed simulated nuclear monitoring and decommissioning style tasks to compare VR and traditional teleoperation interface designs. We found that operators in the VR condition took longer to complete the experiment, had reduced collisions, and rated the generated 3D map with higher importance when compared to non-VR operators. Additional physiological data suggested that VR operators had a lower objective cognitive workload during the experiment but also experienced increased physical demand. Overall the presented results show that VR interfaces may benefit work patterns in teleoperation tasks within the nuclear industry, but further work is needed to investigate how such interfaces can be integrated into real world decommissioning workflows.
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Journal articleMeng J, Yao X, Hong X, et al., 2023,
A solution-to-solid conversion chemistry enables ultrafast-charging and long-lived molten salt aluminium batteries
, NATURE COMMUNICATIONS, Vol: 14 -
Journal articleSetti W, Vitali H, Campus C, et al., 2023,
Audio-Corsi: a novel system to evaluate audio-spatial memory skills.
, Annu Int Conf IEEE Eng Med Biol Soc, Vol: 2023, Pages: 1-4Spatial memory (SM) is a multimodal representation of the external world, which different sensory inputs can mediate. It is essential in accomplishing everyday activities and strongly correlates with sleep processes. However, despite valuable knowledge of the spatial mechanisms in the visual modality, the multi-sensory aspects of SM have yet to be thoroughly investigated due to a lack of proper technologies.This work presents a novel acoustic system built around 3D audio spatial technology. Our goal was to examine if an afternoon nap can improve memory performance, measured through the acoustic version of the Corsi Block Tapping Task (CBTT), named Audio-Corsi. We tested five adults over two days. During one of the two days (Wake), participants performed the Audio-Corsi before (Pre) and after (Post) a wake resting period; while the other day (Sleep), participants performed the Audio-Corsi before (Pre) and after (Post) a nap. Day orders were randomized. We calculated the memory span for the Pre and Post session in both the Wake and Sleep days. Preliminary results show a significant difference in the memory span between the Wake and Sleep days. Specifically, memory span decreased between the pre-and post-test during the wake day. The opposite trend was found for the sleep day. Results indicate that SM can be improved by sleeping also in the acoustic modality other than the visual one.Clinical Relevance- The technology and procedure we designed and developed could be suitable in clinical and experimental settings to study high-level cognitive skills in the auditory sensory modality and their relationship with sleep, especially when vision is absent or distorted (i.e. blindness).
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Journal articleTassell C, Aurisicchio M, 2023,
Refill at home for fast-moving consumer goods: uncovering compliant and divergent consumer behaviour
, Sustainable Production and Consumption, Vol: 39, Pages: 1-16, ISSN: 2352-5509Context and problemConsumers of fast-moving consumer goods have become accustomed to a culture of convenience and disposability, cultivating practices that are at odds with recycling, reusing, and reducing. Through the concept of refill, the fast-moving consumer goods industry is moving beyond the disposability and recyclability of packaging and products to consider longer term, more durable reuse solutions. If practised as intended, reuse has the capacity to lower the intensity of materials used compared to disposal or recycling. However, research on actual reuse behaviour is sparse, and new work is necessary to explore how consumers handle material resources in reuse offerings.MethodIn-depth interviews with 26 consumers were conducted where the behaviour chain method was used to elicit and map resource journeys for 48 refill at home cases.ResultsConsumers of refill at home offerings were found to display both compliant behaviour and a range of divergent resource handling behaviours, which either increased or decreased the impact of reuse. The behaviours were structured in a framework consisting of six reuse resource handling behaviour types and 17 sub-types, which operate alone or in combination. Whilst consumers displayed many instances of compliant behaviour, overall divergent behaviours were more common, like using multiple reusable products for the same purpose or using single-use products in parallel. Interestingly, consumers of refill at home offerings with a service engaged in compliant behaviour in the majority of the instances. Consumers were found to employ divergent behaviours even at the end of life, often recycling non-recyclable reusable components and occasionally disposing of recyclables in residual waste.ConclusionsThe resulting framework of resource handling behaviours provides a more nuanced understanding of reuse in practice than previously offered. The behaviour chain method was found to have the structural and analytical rigour to dissect dif
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Conference paperZhu L, Li N, 2023,
Springback prediction for sheet metal cold stamping using convolutional neural networks
, 2022 Workshop on Electronics Communication Engineering (WECE 2022), Publisher: SPIE, Pages: 1-6Springback is a crucial factor in cold stamping that causes geometric inaccuracy of the stamped component after removal of tools. This study, for the first time, presents a novel application of a Convolutional Neural Network (CNN) based surrogate model to predict the thinning and springback behaviours for cold stamping. Datasets were created based on two cold stamping case studies, i.e., a U-bending case and an outer car door panel stamping case. The datasets were then applied to train the CNN-based surrogate models. The results show that the surrogate models can achieve near indistinguishable full-field predictions in real-time when compared with the FE simulation results. The application of CNN in efficient springback prediction can be adopted in industrial settings to aid both conceptual and final component designs for designers without having manufacturing knowledge.
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Conference paperMalone L, Cardin M-A, Cilliers JJ, et al., 2023,
Exploring Novel Architectures in Lunar In-Situ Resource Utilisation
, Brisbane, Australia, 26th World Mining Congress -
Conference paperBermudez J, Nyrup R, Deterding S, et al., 2023,
What is a subliminal technique? An ethical perspective on AI-driven influence
, 2023 IEEE International Symposium on Ethics in Science, Technology and Engineering, Publisher: IEEE, Pages: 1-10Concerns about threats to human autonomy feature prominently in the field of AI ethics. One aspect of this concern relates to the use of AI systems for problematically manipulative influence. In response to this, the European Union's draft AI Act (AIA) includes a prohibition on AI systems deploying subliminal techniques that alter people's behavior in ways that are reasonably likely to cause harm (Article 5(1)(a)). Critics have argued that the term ‘subliminal techniques’ is too narrow to capture the target cases of AI-based manipulation. We propose a definition of ‘subliminal techniques’ that (a) is grounded on a plausible interpretation of the legal text; (b) addresses all or most of the underlying ethical concerns motivating the prohibition; (c) is defensible from a scientific and philosophical perspective; and (d) does not over-reach in ways that impose excessive administrative and regulatory burdens. The definition provides guidance for design teams seeking to pursue responsible and ethically aligned AI innovation.
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Conference paperArmitage J, Magnusson T, McPherson A, 2023,
Design process in visual programming: methods for visual and temporal analysis
, Sound and Music Computing Conference, Pages: 6-13Visual programming languages, such as Pure Data (Pd)and Max/MSP, have been prevalent in computer music fornearly three decades. However, few shared and consistent research methods have emerged for studying the reproducible use of digital musical instrument (DMI) designers employing these languages. In this paper, we introduce straightforward methods for extracting design process data from Pd usage through automated version control and protocol-based annotation. This data enables visual and temporal analysis, which can reveal patterns of DMI design cognition and collaboration processes. Although our focus is on design, we believe that this approach could also benefit creativity studies and musicological analysis of the compositional process. We present the outcomes ofa study involving four groups of DMI designers in a one-hour closed activity and demonstrate how these analysismethods can be used to gain additional insight by comparing them against participant survey data. In discussing how these methods could be enhanced and further developed, we address validity, scalability, replicability, and generalisability. Lastly, we examine motivations and challenges for DMI design cognition research.
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Conference paperArmitage J, Magnusson T, McPherson A, 2023,
Sculpting algorithmic pattern: informal and visuospatial interaction in musical instrument design
, Sound and Music Computing Conference, Pages: 377-384In live coding, the concept of algorithmic patterns is employed to characterise the improvisation of artistic structures. This paper presents a digital musical instrument(DMI) design study that led to the development of our understanding of proto-algorithmic thinking. The study focused on tools for manually sculpting digital resonancemodels using clay, with participants following brief technical instructions. The resulting thought process was grounded in systematic embodied interaction with the clay, giving rise to a form of algorithmic thinking that precedes the conceptual formalisation of the algorithm. We propose the term ‘proto-algorithmic pattern’ to encompass implicit, tacit, gestural, and embodied practices that lack a formalised notational language. In conclusion, we explore the implications of our findings for interfaces and instruments in live coding and identify potential avenues for future research at the intersection of live coding and DMI design.
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Journal articleGong Y, Li J, Yang K, et al., 2023,
Towards Practical Application of Li-S Battery with High Sulfur Loading and Lean Electrolyte: Will Carbon-Based Hosts Win This Race?
, Nanomicro Lett, Vol: 15As the need for high-energy-density batteries continues to grow, lithium-sulfur (Li-S) batteries have become a highly promising next-generation energy solution due to their low cost and exceptional energy density compared to commercially available Li-ion batteries. Research into carbon-based sulfur hosts for Li-S batteries has been ongoing for over two decades, leading to a significant number of publications and patents. However, the commercialization of Li-S batteries has yet to be realized. This can be attributed, in part, to the instability of the Li metal anode. However, even when considering just the cathode side, there is still no consensus on whether carbon-based hosts will prove to be the best sulfur hosts for the industrialization of Li-S batteries. Recently, there has been controversy surrounding the use of carbon-based materials as the ideal sulfur hosts for practical applications of Li-S batteries under high sulfur loading and lean electrolyte conditions. To address this question, it is important to review the results of research into carbon-based hosts, assess their strengths and weaknesses, and provide a clear perspective. This review systematically evaluates the merits and mechanisms of various strategies for developing carbon-based host materials for high sulfur loading and lean electrolyte conditions. The review covers structural design and functional optimization strategies in detail, providing a comprehensive understanding of the development of sulfur hosts. The review also describes the use of efficient machine learning methods for investigating Li-S batteries. Finally, the outlook section lists and discusses current trends, challenges, and uncertainties surrounding carbon-based hosts, and concludes by presenting our standpoint and perspective on the subject.
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Conference paperDaugintis R, Barumerli R, Picinali L, et al., 2023,
Classifying non-individual head-related transfer functions with a computational auditory model: calibration and metrics
, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 1-5This study explores the use of a multi-feature Bayesian auditory sound localisation model to classify non-individual head-related transfer functions (HRTFs). Based on predicted sound localisation performance, these are grouped into ‘good’ and ‘bad’, and the ‘best’/‘worst’ is selected from each category. Firstly, we present a greedy algorithm for automated individual calibration of the model based on the individual sound localisation data. We then discuss data analysis of predicted directional localisation errors and present an algorithm for categorising the HRTFs based on the localisation error distributions within a limited range of directions in front of the listener. Finally, we discuss the validity of the classification algorithm when using averaged instead of individual model parameters. This analysis of auditory modelling results aims to provide a perceptual foundation for automated HRTF personalisation techniques for an improved experience of binaural spatial audio technologies.
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Conference paperPauwels J, Picinali L, 2023,
On the relevance of the differences between HRTF measurement setups for machine learning
, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 1-5As spatial audio is enjoying a surge in popularity, data-driven machine learning techniques that have been proven successful in other domains are increasingly used to process head-related transfer function measurements. However, these techniques require much data, whereas the existing datasets are ranging from tens to the low hundreds of datapoints. It therefore becomes attractive to combine multiple of these datasets, although they are measured under different conditions. In this paper, we first establish the common ground between a number of datasets, then we investigate potential pitfalls of mixing datasets. We perform a simple experiment to test the relevance of the remaining differences between datasets when applying machine learning techniques. Finally, we pinpoint the most relevant differences.
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Journal articlePedersen RL, Picinali L, Kajs N, et al., 2023,
Virtual-Reality-Based Research in Hearing Science: A Platforming Approach
, AES: Journal of the Audio Engineering Society, Vol: 71, Pages: 374-389, ISSN: 1549-4950The lack of ecological validity in clinical assessment, as well as the challenge of investigating multimodal sensory processing, remain key challenges in hearing science. Virtual Reality (VR) can support hearing research in these domains by combining experimental control with situational realism. However, the development of VR-based experiments is traditionally highly resource demanding, which places a significant entry barrier for basic and clinical researchers looking to embrace VR as the research tool of choice. The Oticon Medical Virtual Reality (OMVR) experiment platform fast-tracks the creation or adaptation of hearing research experiment templates to be used to explore areas such as binaural spatial hearing, multimodal sensory integration, cognitive hearing behavioral strategies, auditory-visual training, etc. In this paper, the OMVR’s functionalities, architecture, and key elements of implementation are presented, important performance indicators are characterized, and a use-case perceptual evaluation is presented.
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Journal articleFaria AS, Soares T, Orlandini T, et al., 2023,
P2P market coordination methodologies with distribution grid management
, Sustainable Energy, Grids and Networks, Vol: 34As prosumers and energy communities gain prominence in power systems, energy trading between prosumers in local P2P markets is paramount. Within this novel market design, peers can directly exchange energy with each other, leading to economic advantages while supporting the decarbonization of the sector. To ensure that voltage and congestion issues are properly addressed, a thorough coordination between the P2P market and the Distribution System Operator is required. This paper presents and compares three mutual-benefit coordination methods. The first method entails applying product differentiation on an iterative basis to avoid exceeding the lines thermal limits, which is performed through penalties on P2P exchanges that may be overloading the network. The second method uses the P2P market with an AC-OPF, ensuring network operation through a flexibility market via upward and downward flexibility. The last one proposes an integrated operation of the P2P market with AC-OPF. All methods are assessed in a typical distribution network with high prosumers integration. The results show that the second method is the one that, fulfilling the network constraints, presents greater social welfare.
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