Adaptation to altered interaural time differences in a virtual reality environment

Interaural time differences (ITDs) are important cues for determining the azimuth location of a sound source and need to be accurately reproduced, in a virtual reality (VR) environment, to achieve a realistic sense of sound location for the listener. ITDs are usually included in head related transfer functions (HRTFs) used for audio rendering, and can be individualised to match the user’s head size (e.g. longer ITDs are needed for larger head sizes). In recent years, studies have shown that it is possible to train subjects to adapt and improve their performance in sound localisation skills to non-individualized HRTFs. The analysis of such improvements has focused mainly on adaptation to monoaural spectral cues rather than binaural cues such as ITDs. In this work listeners are placed in a VR environment and are asked to localise the source of a noise burst in the horizontal plane. Using a generic non-individualized HRTF with its ITD modified to match the head size of each participant, test and training phases are alternated, with the latter providing continuous auditory feedback. The experiment is then repeated with ITDs simulating larger (150%) and smaller (50%) head sizes. Comparing localisation accuracy before and after training, it is observed that while training seems to improve sound localisation performance, this varies according to the simulated head size and target location

Relationships between hearing loss and hearing aid usage in real world

Many hearing aids are fitted with multiple programs that the user can choose between in different situations. In the H2020 EVOTION project hundreds of hearing aid users were fitted according to their audiogram with Oticon VAC rationale with four adaptive programs that differed in the noise management profile. The programs differed on how much the noise was attenuated and the threshold at which the device started to remove noise. The hearing aids also transmitted continuous data about the sound environment and the operation of the hearing aid to a dedicated app on a smartphone. The participants had a wide range of hearing losses and was recruited from amongst new and experienced hearing aid users in six different clinics in UK, Greece, and Denmark. The data from the hearing aid users was collected for up to a year to investigate relationships between clinical factors and usage patterns. The data enables a detailed investigation of the complexity of sound environments throughout the day, relation between complexity as function of time to the clinical factors including and beyond the audiogram

Public health policy-making for hearing loss: stakeholders' evaluation of a novel eHealth tool

Background: Hearing loss (HL) affects 466 million people of all ages worldwide, with a rapidly increasing prevalence, and therefore requires appropriate public health policies. Multi-disciplinary approaches that make use of eHealth services can build the evidence to influence public policy. The European Union-funded project EVOTION developed a platform that is fed with real-time data from hearing aids, a smartphone, and additional clinical data and makes public health policy recommendations based on hypothetical public health policy-making models, a big data engine and decision support system. The present study aimed to evaluate this platform as a new tool to support policy-making for HL. / Methods: A total of 23 key stakeholders in the United Kingdom, Croatia, Bulgaria and Poland evaluated the platform according to the Strengths, Weaknesses, Opportunities and Threats methodology. / Results: There was consensus that the platform, with its advanced technology as well as the amount and variety of data that it can collect, has huge potential to inform commissioning decisions, public health regulations and affect healthcare as a whole. To achieve this, several limitations and external risks need to be addressed and mitigated. Differences between countries highlighted that the EVOTION tool should be used and managed according to local constraints to maximise success. / Conclusion: Overall, the EVOTION platform can equip HL policy-makers with a novel data-driven tool that can support public health policy-making for HL in the future

Clinical validation of a public health policy-making platform for hearing loss (EVOTION): protocol for a big data study

INTRODUCTION: The holistic management of hearing loss (HL) requires an understanding of factors that predict hearing aid (HA) use and benefit beyond the acoustics of listening environments. Although several predictors have been identified, no study has explored the role of audiological, cognitive, behavioural and physiological data nor has any study collected real-time HA data. This study will collect ‘big data’, including retrospective HA logging data, prospective clinical data and real-time data via smart HAs, a mobile application and biosensors. The main objective is to enable the validation of the EVOTION platform as a public health policy-making tool for HL. METHODS AND ANALYSIS: This will be a big data international multicentre study consisting of retrospective and prospective data collection. Existing data from approximately 35 000 HA users will be extracted from clinical repositories in the UK and Denmark. For the prospective data collection, 1260 HA candidates will be recruited across four clinics in the UK and Greece. Participants will complete a battery of audiological and other assessments (measures of patient-reported HA benefit, mood, cognition, quality of life). Patients will be offered smart HAs and a mobile phone application and a subset will also be given wearable biosensors, to enable the collection of dynamic real-life HA usage data. Big data analytics will be used to detect correlations between contextualised HA usage and effectiveness, and different factors and comorbidities affecting HL, with a view to informing public health decision-making. ETHICS AND DISSEMINATION: Ethical approval was received from the London South East Research Ethics Committee (17/LO/0789), the Hippokrateion Hospital Ethics Committee (1847) and the Athens Medical Center’s Ethics Committee (KM140670). Results will be disseminated through national and international events in Greece and the UK, scientific journals, newsletters, magazines and social media. Target audiences include HA users, clinicians, policy-makers and the general public. TRIAL REGISTRATION NUMBER: NCT03316287; Pre-results

Damage Detection Using Blind Source Separation Techniques

Blind source separation (BSS) techniques are applied in many domains since they allow separating a set of signals from their observed mixture without the knowledge (or with very little knowledge) of the source signals or the mixing process. Two particular BSS techniques called Second-Order Blind Identification (SOBI) and Blind Modal Identification (BMID) are considered in this paper for the purpose of structural damage detection or fault diagnosis in mechanical systems. As shown on experimental examples, the BMID method reveals significant advantages. In addition, it is demonstrated that damage detection results may be improved significantly with the help of the block Hankel matrix. The main advantage in this case is that damage detection still remains possible when the number of available sensors is small or even reduced to one. Damage detection is achieved by comparing the subspaces between the reference (healthy) state and a current state through the concept of subspace angle. The efficiency of the methods is illustrated using experimental data

Separation of acoustic emission signals from small size multi-cylinder diesel engine

This paper presents techniques which can lead to diagnosis of faults in a small size multi-cylinder diesel engine. Preliminary analysis of the acoustic emission (AE) signals is outline, including time-frequency analysis and selection of optimum frequency band.The results of applying mean field independent component analysis (MFICA) to separate the AE root mean square (RMS) signals and the effects of changing parameter values are also outlined. The results on separation of RMS signals show thsi technique has the potential of increasing the probability to successfully identify the AE events associated with the various mechanical events within the combustion process of multi-cylinder diesel engines

Acoustic emission for diesel engine monitoring: A review and preliminary

Vibration analysis has been a prime tool in condition monitoring of rotating machines, however, its application to internal combustion engines remains a challenge because engine vibration signatures are highly non-stationary that are not suitable for popular spectrum-based analysis. Signal-to-noise ratio is a main concern in engine signature analysis due to severe background noise being generated by consecutive mechanical events, such as combustion, valve opening and closing, especially in multi-cylinder engines. Acoustic Emission (AE) has been found to give excellent signal-to-noise ratio allowing discrimination of fine detail of normal or abnormal events during a given cycle. AE has been used to detect faults, such as exhaust valve leakage, fuel injection behaviour, and aspects of the combustion process. This paper presents a review of AE application to diesel engine monitoring and preliminary investigation of AE signature measured on an 18-cylinder diesel engine. AE is compared with vibration acceleration for varying operating conditions: load and speed. Frequency characteristics of AE from those events are analysed in time-frequency domain via short time Fourier trasform. The result shows a great potential of AE analysis for detection of various defects in diesel engines