Mosquito surveys are time‐consuming, expensive and can provide a biased spatial sample of occurrence data—the data often representing the location of the surveys, not the occurrence of the mosquitoes. We present the HumBug project, an acoustic system that can turn any Android smartphone into a mosquito sensor. Our sensor has the potential to significantly increase the quantity of mosquito occurrence data as well as access locations that are more difficult to survey by traditional means. We describe our database of wild‐captured mosquito fight tone audio data and outline our mosquito detection algorithms that these data train. We also present our MozzWear App, designed to work on budget smartphones, which, together with our HumBug Net (an adapted traditional bednet), facilitates data collection and allows the user to record and directly upload mosquito flight tones from any dwelling with a bednet in the field. Our HumBug system has the potential to vastly increase our understanding of the distribution of mosquito species in space and time and greatly improve surveys needed to assess the success or failure of ongoing vector control measures. At a time when the WHO reports a plateauing in the decade‐long decline in malaria mortality rates, this new technological solution for surveying mosquito vectors will provide a timely new resource.

Demining is a highly impactful but complex problem which requires considerable resources and time. Land mine detection is the most hazardous and time consuming of the tasks in the demining pipeline. Currently, the risk of landmines being present in an area is estimated on the basis of non-technical surveys which are expensive and slow. This paper presents a novel spatial landmine risk prediction model to help and improve the allocation of resources in demining operations and even predict future areas of interest. Our approach is based on training predictive models on geographical and social development data for areas recorded to have been demined in the past. We then use this model to predict areas with high chance of mine presence in the vicinity of the demined area so as to progressively expand the area of operations. We explore weighted classification and biased scoring methods to improve the performance of our base logistic regression and support vector machine models. Refinement of conventional models allows us to tackle the problem of unbalanced datasets in our application. The resulting pipeline is then characterized in terms of various performance metrics. The results show that the pipeline has a potential to provide reliable predictive information based on historic demining data, which can help organizations plan their resource allocations in future demining operations.

The performance of the independent vector analysis (IVA) algorithm depends on the choice of the source prior to better model the speech signals as it employs a multivariate source prior to retain the dependency between frequency bins of each source. Identical source priors are frequently used for the IVA methods; however, different speech sources will generally have different statistical properties. In this work, instead of identical source priors, a novel Student’s t mixture model based source prior is introduced for the IVA algorithm that can adapt to the statistical properties of different speech sources and thereby enhance the separation performance of the IVA algorithm. The unknown parameters of the source prior and unmixing matrices are estimated together by deriving an efficient expectation maximization (EM) algorithm. Useful improvement in the separation performance in different realistic scenarios is confirmed by experimental studies on real datasets.

The independent vector analysis (IVA) algorithm employs a multivariate source prior to retain the dependency between different frequency bins of each source and thereby avoids the permutation problem that is inherent to blind source separation (BSS). In this paper, a multivariate Student's t distribution is adopted as the source prior, which because of its heavy tail nature can better model the large amplitude information in the frequency bins. Therefore it can improve the separation performance and the convergence speed of the IVA and fast version of the IVA (FastIVA) algorithms as compared with the IVA algorithm based on another multivariate super Gaussian source prior. Separation performance with real binaural room impulse responses (BRIRs) is evaluated by detailed simulation studies when using the different source priors, and the experimental results confirm that the IVA and the FastIVA with the proposed multivariate Student's t source prior can consistently achieve improved and faster separation performance.