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Recording multiple sound sources in a reverberant environment results in convolutive mixtures. Sound sources can be extracted from microphone array recordings of such mixtures using acoustic source separation techniques. Acoustic source separation using recordings obtained from rigid spherical microphone arrays (RSMA) benefit from the representation of sound fields as series of spherical harmonics. More specifically, RSMAs afford increased flexibility in acoustic beamforming and spatial filtering. We propose a data-driven DOA estimation and acoustic source separation method based on a dictionary-based sparse decomposition of sound fields. The proposed method involves identifying the time-frequency bins with contributions from a single source only and those with sensor noise or diffuse sound field components. The former set of bins is used in DOA estimation and beamforming in the sparse decomposition domain. The latter set is used to calculate the diffuse field covariance matrix used in Wiener post-filtering to improve the source separation performance further. We demonstrate the utility of the proposed method via extensive objective and subjective evaluations.

The novel coronavirus pandemic has negative impacts over the health, economy and well-being of the global population. This negative effect is growing with the high spreading rate of the virus. The most critical step to prevent the spreading of the virus is pre-screening and early diagnosis of the individuals. This results in quaranteeing the patients not to effect the healthy population. COVID-19 is the name of the disease caused by the novel coronavirus. It has a high infection rate and it is urgent to diagnose many patients as we can to prevent the spread of the virus at the early stage. Rapid diagnostic tools development is urgent to save lives. MantisCOVID is a cloud-based pre-diagnosis tool to be accessed from the internet. This tool delivers a rapid screening test by analyzing the X-ray Chest Radiograph scans via Artificial Intelligence (AI) and it also evaluates the mortality rate of patients with the synthesis of the patient’s history with the machine learning methods. This study reveals the methods used over the platform and evaluation of the algorithms via open datasets.

Estimation of the direction-of-arrival (DOA) of sound sources is an important step in sound field analysis. Rigid spherical microphone arrays allow the calculation of a compact spherical harmonic representation of the sound field. A basic method for analysing sound fields recorded using such arrays is steered response power (SRP) maps wherein the source DOA can be estimated as the steering direction that maximises the output power of a maximally-directive beam. This approach is computationally costly since it requires steering the beam in all possible directions. This paper presents an extension to SRP called steered response power density (SRPD) and an associated, signal-adaptive search method called hierarchical grid refinement (HiGRID) for reducing the number of steering directions needed for DOA estimation. The proposed method can localise coherent as well as incoherent sources while jointly providing the number of prominent sources in the scene. It is shown to be robust to reverberation and additive white noise. An evaluation of the proposed method using simulations and real recordings under highly reverberant conditions as well as a comparison with state- of-the-art methods are presented., 14 pages, 10 figures, 4 tables, submitted to IEEE/ACM Transactions on Audio, Speech and Language Processing (03 March 2018)

Blood cell classification is a recent topic for scientists working on the diagnosis of blood cell related illnesses. As the number of computer vision (CV) applications is increasing to improve quality of human life, it spreads in the areas of autonomous drive, surveillance, robotic applications, telecommunications and etc. The number of CV applications also increases in the medical sector due the decreasing value of doctors per patient ratio (DPPR) in urban and suburban areas. A doctor working in such areas sometimes would have to interpret thousands of patients’ test results in a day. This condition would result disadvantages such as false diagnosis on patients and break on working motivations for doctors. Some of the tests would probably be interpreted using an application developed by Artificial Neural Networks (ANN). Tests related to blood cells are examined for the patients as a starting point of diagnosis and information obtained about their abnormalities give doctors a preliminary idea about the illnesses. This article issues generation of a CV application that would be used as an assistant of doctors who have domain expertise. The article issues segmentation of blood cells, classification of red and white blood cells containing 6 types such as erythrocyte, lymphocyte, platelets, neutrophil, monocytes and eosinophils using the segmentation results. It also discusses about a method for detection of abnormalities on red blood cells (erythrocyte).

Acoustic source separation refers to the extraction of individual source signals from microphone array recordings of multiple sources made in multipath environments such as rooms. The most straightforward approach to acoustic source separation involves spatial filtering via beamforming. While beamforming works well for a few sources and under low reverberation, its performance diminishes for a high number of sources and/or high reverberation. An informed acoustic source separation method based on the application of orthogonal matching pursuit (OMP) on complex-valued steered response (SR) maps is presented in this paper. It is shown that the proposed method is robust to a high level of reverberation and affords better source separation than maximally directive steered beamforming in terms of signal-to-interference and signal-to-distortion ratios.

This paper explores an effective solution for a test configuration to perform system-level manufacturing tests without use of system management/user interface software of system under test (SUT). A generic, computer aided and modular test configuration, which acts in combination and in an integrated way with the management unit(s) of the SUT over Ethernet connectivity is proposed as a solution. Using enough number of software modules to be installed on the management unit(s), the proposed test configuration provides a number of improvements compared to the existing test configurations. Parallel testing capability is one of these major improvements meaning that test scenarios may be applied to a certain number of SUTs at the same time independently. This feature accelerates the system-level manufacturing test activities both during the planned test operations and during fault localization cases. The proposed configuration is implemented and used on various systems based on various platforms such as; sheltered C4I platforms, tracked observer or gun platforms, wheeled ground surveying platforms etc.