Speech Separation Using Partially Asynchronous Microphone Arrays Without Resampling

We consider the problem of separating speech sources captured by multiple spatially separated devices, each of which has multiple microphones and samples its signals at a slightly different rate. Most asynchronous array processing methods rely on sample rate offset estimation and resampling, but these offsets can be difficult to estimate if the sources or microphones are moving. We propose a source separation method that does not require offset estimation or signal resampling. Instead, we divide the distributed array into several synchronous subarrays. All arrays are used jointly to estimate the time-varying signal statistics, and those statistics are used to design separate time-varying spatial filters in each array. We demonstrate the method for speech mixtures recorded on both stationary and moving microphone arrays.Comment: To appear at the International Workshop on Acoustic Signal Enhancement (IWAENC 2018

Development assistance gone wrong : why support services have failed to expand exports

This study shows that in developing countries with no more than partly favorable policies toward manufactured exports, outside assistance to services that promote and support manufactured exports has had little discernible impact on exports and has rarely been effective in expanding them. The principal reasons for this lack of impact appear to be the after effects of inward-looking development policies, neglect of assistance to enterprises in the production and supply aspects of exporting, insufficient donor concern about the direct impact of their assistance on exports, and reliance on an inappropriate delivery mechanism. Recommendations which suggest new guidelines for donor assistance, project components and new country policies are explained in a companion paper, WPS 544.Economic Theory&Research,Environmental Economics&Policies,Health Economics&Finance,ICT Policy and Strategies,Poverty Assessment

Acoustic Impulse Responses for Wearable Audio Devices

We present an open-access dataset of over 8000 acoustic impulse from 160 microphones spread across the body and affixed to wearable accessories. The data can be used to evaluate audio capture and array processing systems using wearable devices such as hearing aids, headphones, eyeglasses, jewelry, and clothing. We analyze the acoustic transfer functions of different parts of the body, measure the effects of clothing worn over microphones, compare measurements from a live human subject to those from a mannequin, and simulate the noise-reduction performance of several beamformers. The results suggest that arrays of microphones spread across the body are more effective than those confined to a single device.Comment: To appear at ICASSP 201

Linear MMSE-Optimal Turbo Equalization Using Context Trees

Formulations of the turbo equalization approach to iterative equalization and decoding vary greatly when channel knowledge is either partially or completely unknown. Maximum aposteriori probability (MAP) and minimum mean square error (MMSE) approaches leverage channel knowledge to make explicit use of soft information (priors over the transmitted data bits) in a manner that is distinctly nonlinear, appearing either in a trellis formulation (MAP) or inside an inverted matrix (MMSE). To date, nearly all adaptive turbo equalization methods either estimate the channel or use a direct adaptation equalizer in which estimates of the transmitted data are formed from an expressly linear function of the received data and soft information, with this latter formulation being most common. We study a class of direct adaptation turbo equalizers that are both adaptive and nonlinear functions of the soft information from the decoder. We introduce piecewise linear models based on context trees that can adaptively approximate the nonlinear dependence of the equalizer on the soft information such that it can choose both the partition regions as well as the locally linear equalizer coefficients in each region independently, with computational complexity that remains of the order of a traditional direct adaptive linear equalizer. This approach is guaranteed to asymptotically achieve the performance of the best piecewise linear equalizer and we quantify the MSE performance of the resulting algorithm and the convergence of its MSE to that of the linear minimum MSE estimator as the depth of the context tree and the data length increase.Comment: Submitted to the IEEE Transactions on Signal Processin

Investigating periphyton biofilm response to changing phosphorus concentrations in UK rivers using within-river flumes

The excessive growth of benthic algal biofilms in UK rivers is a widespread problem, resulting in loss of plant communities and wider ecological damage. Elevated nutrient concentrations (particularly phosphorus) are often implicated, as P is usually considered the limiting nutrient in most rivers. Phosphorus loadings to rivers in the UK have rapidly decreased in the last decade,due to improvements in sewage treatment and changes to agricultural practises. However, in many cases, these improvements in water quality have not resulted in a reduction in nuisance algal growth. It is therefore vital that catchment managers know what phosphorus concentrations need to be achieved, in order to meet the UK’s obligations to attain good ecological status, under the EU’s Water Framework Directive. This study has developed a novel methodology, using within river mesocosms, which allows P concentrations of river water to be either increased or decreased, and the effect on biofilm accrual rate is quantified. These experiments identify the phosphorus concentrations at which algae becomes P-limited, which can be used to determine knowledge-based P targets for rivers. The ability to reduce P concentrations in river water enables algae–nutrient limitation to be studied in nutrient-enriched rivers for the first time

Personalized medicine : the impact on chemistry

An effective strategy for personalized medicine requires a major conceptual change in the development and application of therapeutics. In this article, we argue that further advances in this field should be made with reference to another conceptual shift, that of network pharmacology. We examine the intersection of personalized medicine and network pharmacology to identify strategies for the development of personalized therapies that are fully informed by network pharmacology concepts. This provides a framework for discussion of the impact personalized medicine will have on chemistry in terms of drug discovery, formulation and delivery, the adaptations and changes in ideology required and the contribution chemistry is already making. New ways of conceptualizing chemistry’s relationship with medicine will lead to new approaches to drug discovery and hold promise of delivering safer and more effective therapies