Model-Based Speech Enhancement in the Modulation Domain

This paper presents an algorithm for modulationdomain speech enhancement using a Kalman filter. The proposed estimator jointly models the estimated dynamics of the spectral amplitudes of speech and noise to obtain an MMSE estimation of the speech amplitude spectrum with the assumption that the speech and noise are additive in the complex domain. In order to include the dynamics of noise amplitudes with those of speech amplitudes, we propose a statistical “Gaussring” model that comprises a mixture of Gaussians whose centres lie in a circle on the complex plane. The performance of the proposed algorithm is evaluated using the perceptual evaluation of speech quality (PESQ) measure, segmental SNR (segSNR) measure and shorttime objective intelligibility (STOI) measure. For speech quality measures, the proposed algorithm is shown to give a consistent improvement over a wide range of SNRs when compared to competitive algorithms. Speech recognition experiments also show that the Gaussring model based algorithm performs well for two types of noise

Measuring audio-visual speech intelligibility under dynamic listening conditions using virtual reality

The ELOSPHERES project is a collaboration between researchers at Imperial College London and University College London which aims to improve the efficacy of hearing aids. The benefit obtained from hearing aids varies significantly between listeners and listening environments. The noisy, reverberant environments which most people find challenging bear little resemblance to the clinics in which consultations occur. In order to make progress in speech enhancement, algorithms need to be evaluated under realistic listening conditions. A key aim of ELOSPHERES is to create a virtual reality-based test environment in which alternative speech enhancement algorithms can be evaluated using a listener-in-the-loop paradigm. In this paper we present the sap-elospheres-audiovisual-test (SEAT) platform and report the results of an initial experiment in which it was used to measure the benefit of visual cues in a speech intelligibility in spatial noise task

Phase-aware single-channel speech enhancement with modulation-domain Kalman filtering

We present a speech enhancement algorithm that performs modulation-domain Kalman filtering to track the speech phase using circular statistics, along with the log-spectra of speech and noise. In the proposed algorithm, the speech phase posterior is used to create an enhanced speech phase spectrum for the signal reconstruction of speech. The Kalman filter prediction step separately models the temporal inter-frame correlation of the speech and noise spectral log-amplitudes and of the speech phase, while the Kalman filter update step models their nonlinear relations under the assumption that speech and noise add in the complex short-time Fourier transform domain. The phase-sensitive enhancement algorithm is evaluated with speech quality and intelligibility metrics, using a variety of noise types over a range of SNRs. Instrumental measures predict that tracking the speech log-spectrum and phase with modulation-domain Kalman filtering leads to consistent improvements in speech quality, over both conventional enhancement algorithms and other algorithms that perform modulation-domain Kalman filtering

Determining biosensing modes in SH-SAW device using 3D finite element analysis

Surface acoustic wave (SAW) sensors are electromechanical devices that exploit the piezoelectric effect to induce elastic (acoustic) waves which are sensitive to small perturbations: for example specific binding and recognition of disease biomarkers. Shear horizontal surface acoustic waves (SH-SAWs) are particularly suited to biosample analysis as the wave is not completely radiated and lost into the liquid medium (e.g., blood, saliva) as is the case, for example, in a device implementing Rayleigh waves. Here, using 3D finite element analysis (FEA) the nature of waves launched on a particular quartz device is investigated with respect to the cut of the quartz, the addition of gold guiding layers, and the addition of other linear elastic materials of contrasting acoustic properties. It is demonstrated that 3D FEA analysis showing the device's frequency shift with added guiding layer height reveals a proportional relationship in agreement with the Sauerbrey equation from perturbation theory. It is directly shown, given certain device parameters and a gold guiding layer, that shear horizontally polarized waves are launched on the surface with a dominant mode frequency around 250 MHz. This would be an appropriate biosensing mode in Point of Care (POC) testing for the particular properties of certain disease biomarkers delivered via a liquid medium

Localization Experiments with Reporting by Head Orientation: Statistical Framework and Case Study

This research focuses on sound localization experiments in which subjects report the position of an active sound source by turning toward it. A statistical framework for the analysis of the data is presented together with a case study from a large-scale listening experiment. The statistical framework is based on a model that is robust to the presence of front/back confusions and random errors. Closed-form natural estimators are derived, and one-sample and two-sample statistical tests are described. The framework is used to analyze the data of an auralized experiment undertaken by nearly nine hundred subjects. The objective was to explore localization performance in the horizontal plane in an informal setting and with little training, which are conditions that are similar to those typically encountered in consumer applications of binaural audio. Results show that responses had a rightward bias and that speech was harder to localize than percussion sounds, which are results consistent with the literature. Results also show that it was harder to localize sound in a simulated room with a high ceiling despite having a higher direct-to-reverberant ratio than other simulated rooms

Admission to hospital following head injury in England: Incidence and socio-economic associations

BACKGROUND: Head injury in England is common. Evidence suggests that socio-economic factors may cause variation in incidence, and this variation may affect planning for services to meet the needs of those who have sustained a head injury. METHODS: Socio-economic data were obtained from the UK Office for National Statistics and merged with Hospital Episodes Statistics obtained from the Department of Health. All patients admitted for head injury with ICD-10 codes S00.0–S09.9 during 2001–2 and 2002–3 were included and collated at the level of the extant Health Authorities (HA) for 2002, and Primary Care Trust (PCT) for 2003. Incidence was determined, and cluster analysis and multiple regression analysis were used to look at patterns and associations. Results: 112,718 patients were admitted during 2001–2 giving a hospitalised incidence rate for England of 229 per 100,000. This rate varied across the English HA's ranging from 91–419 per 100,000. The rate remained unchanged for 2002–3 with a similar magnitude of variation across PCT's. Three clusters of HA's were identified from the 2001–2 data; those typical of London, those of the Shire counties, and those of Other Urban authorities. Socio-economic factors were found to account for a high proportion of the variance in incidence for 2001–2. The same pattern emerged for 2002–3 at the PCT level. The use of public transport for travel to work is associated with a decreased incidence and lifestyle indicators, such as the numbers of young unemployed, increase the incidence. CONCLUSION: Head injury incidence in England varies by a factor of 4.6 across HA's and PCT's. Planning head injury related services at the local level thus needs to be based on local incidence figures rather than regional or national estimates. Socio-economic factors are shown to be associated with admission, including travel to work patterns and lifestyle indicators, which suggests that incidence is amenable to policy initiatives at the macro level as well as preventive programmes targeted at key groups

Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal

Mounting evidence suggests that there is frequently considerable variation in the risk of the outcome of interest in clinical trial populations. These differences in risk will often cause clinically important heterogeneity in treatment effects (HTE) across the trial population, such that the balance between treatment risks and benefits may differ substantially between large identifiable patient subgroups; the "average" benefit observed in the summary result may even be non-representative of the treatment effect for a typical patient in the trial. Conventional subgroup analyses, which examine whether specific patient characteristics modify the effects of treatment, are usually unable to detect even large variations in treatment benefit (and harm) across risk groups because they do not account for the fact that patients have multiple characteristics simultaneously that affect the likelihood of treatment benefit. Based upon recent evidence on optimal statistical approaches to assessing HTE, we propose a framework that prioritizes the analysis and reporting of multivariate risk-based HTE and suggests that other subgroup analyses should be explicitly labeled either as primary subgroup analyses (well-motivated by prior evidence and intended to produce clinically actionable results) or secondary (exploratory) subgroup analyses (performed to inform future research). A standardized and transparent approach to HTE assessment and reporting could substantially improve clinical trial utility and interpretability

Multivariable risk prediction can greatly enhance the statistical power of clinical trial subgroup analysis

BACKGROUND: When subgroup analyses of a positive clinical trial are unrevealing, such findings are commonly used to argue that the treatment's benefits apply to the entire study population; however, such analyses are often limited by poor statistical power. Multivariable risk-stratified analysis has been proposed as an important advance in investigating heterogeneity in treatment benefits, yet no one has conducted a systematic statistical examination of circumstances influencing the relative merits of this approach vs. conventional subgroup analysis. METHODS: Using simulated clinical trials in which the probability of outcomes in individual patients was stochastically determined by the presence of risk factors and the effects of treatment, we examined the relative merits of a conventional vs. a "risk-stratified" subgroup analysis under a variety of circumstances in which there is a small amount of uniformly distributed treatment-related harm. The statistical power to detect treatment-effect heterogeneity was calculated for risk-stratified and conventional subgroup analysis while varying: 1) the number, prevalence and odds ratios of individual risk factors for risk in the absence of treatment, 2) the predictiveness of the multivariable risk model (including the accuracy of its weights), 3) the degree of treatment-related harm, and 5) the average untreated risk of the study population. RESULTS: Conventional subgroup analysis (in which single patient attributes are evaluated "one-at-a-time") had at best moderate statistical power (30% to 45%) to detect variation in a treatment's net relative risk reduction resulting from treatment-related harm, even under optimal circumstances (overall statistical power of the study was good and treatment-effect heterogeneity was evaluated across a major risk factor [OR = 3]). In some instances a multi-variable risk-stratified approach also had low to moderate statistical power (especially when the multivariable risk prediction tool had low discrimination). However, a multivariable risk-stratified approach can have excellent statistical power to detect heterogeneity in net treatment benefit under a wide variety of circumstances, instances under which conventional subgroup analysis has poor statistical power. CONCLUSION: These results suggest that under many likely scenarios, a multivariable risk-stratified approach will have substantially greater statistical power than conventional subgroup analysis for detecting heterogeneity in treatment benefits and safety related to previously unidentified treatment-related harm. Subgroup analyses must always be well-justified and interpreted with care, and conventional subgroup analyses can be useful under some circumstances; however, clinical trial reporting should include a multivariable risk-stratified analysis when an adequate externally-developed risk prediction tool is available

Quality of medication use in primary care - mapping the problem, working to a solution: a systematic review of the literature

Background: The UK, USA and the World Health Organization have identified improved patient safety in healthcare as a priority. Medication error has been identified as one of the most frequent forms of medical error and is associated with significant medical harm. Errors are the result of the systems that produce them. In industrial settings, a range of systematic techniques have been designed to reduce error and waste. The first stage of these processes is to map out the whole system and its reliability at each stage. However, to date, studies of medication error and solutions have concentrated on individual parts of the whole system. In this paper we wished to conduct a systematic review of the literature, in order to map out the medication system with its associated errors and failures in quality, to assess the strength of the evidence and to use approaches from quality management to identify ways in which the system could be made safer. Methods: We mapped out the medicines management system in primary care in the UK. We conducted a systematic literature review in order to refine our map of the system and to establish the quality of the research and reliability of the system. Results: The map demonstrated that the proportion of errors in the management system for medicines in primary care is very high. Several stages of the process had error rates of 50% or more: repeat prescribing reviews, interface prescribing and communication and patient adherence. When including the efficacy of the medicine in the system, the available evidence suggested that only between 4% and 21% of patients achieved the optimum benefit from their medication. Whilst there were some limitations in the evidence base, including the error rate measurement and the sampling strategies employed, there was sufficient information to indicate the ways in which the system could be improved, using management approaches. The first step to improving the overall quality would be routine monitoring of adherence, clinical effectiveness and hospital admissions. Conclusion: By adopting the whole system approach from a management perspective we have found where failures in quality occur in medication use in primary care in the UK, and where weaknesses occur in the associated evidence base. Quality management approaches have allowed us to develop a coherent change and research agenda in order to tackle these, so far, fairly intractable problems

Cross-protection against European swine influenza viruses in the context of infection immunity against the 2009 pandemic H1N1 virus : studies in the pig model of influenza

Pigs are natural hosts for the same influenza virus subtypes as humans and are a valuable model for cross-protection studies with influenza. In this study, we have used the pig model to examine the extent of virological protection between a) the 2009 pandemic H1N1 (pH1N1) virus and three different European H1 swine influenza virus (SIV) lineages, and b) these H1 viruses and a European H3N2 SIV. Pigs were inoculated intranasally with representative strains of each virus lineage with 6- and 17-week intervals between H1 inoculations and between H1 and H3 inoculations, respectively. Virus titers in nasal swabs and/or tissues of the respiratory tract were determined after each inoculation. There was substantial though differing cross-protection between pH1N1 and other H1 viruses, which was directly correlated with the relatedness in the viral hemagglutinin (HA) and neuraminidase (NA) proteins. Cross-protection against H3N2 was almost complete in pigs with immunity against H1N2, but was weak in H1N1/pH1N1-immune pigs. In conclusion, infection with a live, wild type influenza virus may offer substantial cross-lineage protection against viruses of the same HA and/or NA subtype. True heterosubtypic protection, in contrast, appears to be minimal in natural influenza virus hosts. We discuss our findings in the light of the zoonotic and pandemic risks of SIVs