Bulbar ALS detection based on analysis of voice perturbation and vibrato

On average the lack of biological markers causes a one year diagnostic delay to detect amyotrophic lateral sclerosis (ALS). To improve the diagnostic process an automatic voice assessment based on acoustic analysis can be used. The purpose of this work was to verify the suitability of the sustain vowel phonation test for automatic detection of patients with ALS. We proposed enhanced procedure for separation of voice signal into fundamental periods that requires for calculation of perturbation measurements (such as jitter and shimmer). Also we proposed method for quantitative assessment of pathological vibrato manifestations in sustain vowel phonation. The study's experiments show that using the proposed acoustic analysis methods, the classifier based on linear discriminant analysis attains 90.7% accuracy with 86.7% sensitivity and 92.2% specificity

Instantaneous pitch estimation algorithm based on multirate sampling

The paper presents an algorithm for accurate pitch estimation that takes advantage of the sinusoidal model with instantaneous parameters. The algorithm decomposes the signal into subband components, extracts their instantaneous parameters and evaluates period candidate generating function (PCGF). In order to achieve high accuracy for low and high-pitched sounds it is assumed that possible pitch variation range is proportional to current pitch value. The bandwidths of the decomposition filters and length of the analysis frame are scaled for each period candidate by multirate sampling. The algorithm is compared to other widely used pitch extractors on artificial quasiperiodic signals and natural speech. The proposed algorithm shows a remarkable frequency and time resolution for pitch-modulated sounds and performs well both in clean and noisy conditions

Justified use of 5% amorolfine nail lacquer, in the treatment of toe onychomycosis

The article presents the description of three clinical cases of the successful treatment of toe onychomycosis and athlete’s foot of various etiologies using 5% amorolfine antifungal nail lacquer. The first case: a 31-year-old woman was diagnosed with white superficial onychomycosis of great toe caused by Trichophyton rubrum. The treatment with 5% amorolfine once a week for 6 months resulted in full recovery (both mycological and clinical). The second case: a 42-year-old woman developed onychomycosis after the application of decorative coating on her nails; onychomycosis was caused by Scopulariopsis brevicaulis. She was treated with itraconazole pulse therapy and 5% amorolfine lacquer. She fully recovered. The third case: a 65-year-old man with total onychomycosis of 10 toes developed the skin mycosis of the left foot and lower third of the leg. He was prescribed a therapy with sertaconazole cream and 5% amorolfine lacquer. The use of 5% amorolfine lacquer was continued to prevent from recurrent dermatomycosis. Thus, the above mentioned cases are a good example of the advantages of using 5% amorolfine lacquer in the treatment of most toe onychomycosis types caused by any pathogens (dermatophytes, yeasts or molds)

Deep multi-scale face detector based on deep neural network

Целью настоящей работы являлось проектирование глубокой искусственной нейронной сети для детектирования лиц. Основное внимание при проектировании было уделено обеспечению высокой производительности и уменьшению требуемых вычислительных затрат за счет: 1) факторизации операции свертки; 2) применения точечных сверток; 3) комбинирования поканальных и точечных сверток. Разработанный детектор сравнивался со схожими детекторами лиц, полученными на основе широко распространенных архитектур нейронных сетей MobileNet и NasNet. Предложенная архитектура детектора лиц имеет вычислительную сложность 5.1 MFLOPs, что в два раза меньше, чем у MobileNet (11.7 MFLOPs) и в четыре раза меньше, чем у NasNet (22 MFLOPs). Соответственно время детектирования на изображении 416×416 составило 5.12 мс (или 195 FPS) с видеокарты GeForce 1080 Ti, а также 65.4 мс (или 15 FPS) на одном ядре процессора Intel Core i7-8700K. При этом точность нашей архитектуры равна 85% и уступает MobileNet лишь на 4%, а NasNet – на 9.5%. The main objective of this work was a development of a deep artificial neural network for face detection purposes. The focus of its design was made on providing of the high performance of the detector and lowering of its computational power requirements by using: 1) factorization of convolution; 2) pointwise convolution; 3) combination of depthwise and pointwise convolution. The detector was compared with similar face detectors based on other well-known neural network architectures MobileNet and NasNet. The proposed face detector has a computational complexity equalling 5.1 MFLOPs, which is two times less than MobileNet’s one (11,7 MFLOPs) and four times less than NasNet’s one (22 MFLOPs). The detection time for 416 × 416 image was 5.12 ms (or 195 FPS) using GPU GeForce 1080 Ti, and 65.4 ms (or 15 FPS) using one processor core of Intel Core i7-8700K. The precision of our design is 85% and less on 4% than MobileNet has, and less on 9.5% than NasNet has

АЛГОРИТМ ПОДАВЛЕНИЯ ШУМА И АКУСТИЧЕСКОЙ ОБРАТНОЙ СВЯЗИ НА ОСНОВЕ СПЕКТРАЛЬНОГО ВЫЧИТАНИЯ В СЛУХОВОМ ПРОТЕЗЕ НА БАЗЕ СМАРТФОНА

The paper presents a combined noise and acoustic feedback reduction algorithm. The algorithm is based on spectral subtraction and is robust to rapid changes in acoustic feedback path which makes it suitable for using in a smartphone-based hearing aid.В работе предлагается совмещенный алгоритм подавления шума и акустической обратной связи. Алгоритм основан на спектральном вычитании и является устойчивым к резким изменениям параметров пути распространения акустической обратной связи, что делает его подходящим для использования в слуховых протезах на основе смартфонов

ЦИФРОВЫЕ БАНКИ ФИЛЬТРОВ ДЛЯ СОВРЕМЕННЫХ ЗАДАЧ ОБРАБОТКИ ЗВУКОВЫХ СИГНАЛОВ

The paper reviews techniques of digital filter bank synthesis that can be applied for contemporary speech processing challenges. The paper describes practical experience of using digital filter banks in original systems of sound processing, namely, musical player with noise-aware audio enhancement and hearing aid application for a smartphone.В работе выполнен обзор способов синтеза цифровых банков фильтров, которые могут применяться для решения современных прикладных задач обработки звуковых сигналов. Описывается практический опыт использования цифровых банков фильтров в оригинальных системах обработки звука: музыкальном плеере с функцией повышения разборчивости звучания при прослушивании в шумной акустической обстановке, а также слуховом аппарате на базе смартфона

Highly Sensitive Immunochromatographic Identification of Tetracycline Antibiotics in Milk

A rapid immunochromatographic assay was developed for the control of tetracycline (TC). The assay is based on the competition between immobilized TC-protein conjugate and TC in a tested sample for binding with polyclonal anti-TC antibodies conjugated to colloidal gold during the flow of the sample along a membrane strip with immobilized reactants. Conjugation of colloidal gold and the total immunoglobulin (IgG) fraction of polyclonal antibodies was used to increase the assay sensitivity to ensure low content of specific antibodies in the conjugate. This allowed effective inhibition of free TC and conjugate binding in the strip test zone. Photometric marker registration allows control of the reduction of binding, thereby enhancing detection sensitivity. The proposed assay allows TC to be detected at concentrations up to 20 ng/mL, exceeding the limit of detection of the known analogues, in a wide working range (more than two orders) of 60 pg/mL to 10 ng/mL, ensured through the use of polyclonal antibodies. The assay time is 10 min. The efficiency of the designed assay is shown to identify TC in milk; the degree of recovery of TC ranges from 90 to 112%. The precision of the concentrations measurements was no more than 10%

МЕЖОБЗОРНАЯ КОМПЕНСАЦИЯ ДИСКРЕТНЫХ МЕШАЮЩИХ ОТРАЖЕНИЙ С ФОРМИРОВАНИЕМ КАРТЫ ПОМЕХ И НАКОПЛЕНИЕМ РЕШЕНИЙ

The multi-scan moving target indication with clutter map and storage solutions approach is considered. The estimation performance of compensation devices is compared.Рассмотрен метод межобзорной селекции движущихся целей на фоне дискретных мешающих отражений с формированием карты помех и накоплением решений. Проведен сопоставительный анализ коэффициентов подавления одной карты помех с накоплением решений с двумя совмещенными картами помех для различных условий наблюдения

Acoustic analysis of voice for detection of speech disorder for amyotrophic lateral sclerosis

Рассматривается способ акустического анализа голосового сигнала, содержащего протяжные гласные звуки, для построения системы детектирования речевых нарушений при боковом амиотрофическом склерозе (БАС), являющимся неврологическим заболеванием. Предложен способ сегментации голосового сигнала на периоды основного тона, который используется при расчете параметров джиттер и шиммер. Выполнено сравнение двух систем детектирования речевых нарушений при БАС, в одной из которых исходными данными являлись параметры голоса, полученные предлагаемым способом, а во второй – параметры, полученные в распространенной системе PRAAT. Результаты экспериментов показали, что применение прилагаемого способа анализа значительно улучшает (на 20 %) точность детектирования. A method of acoustic signal analysis with sustain vowel phonation for detection of amyotrophic lateral sclerosis (ALS) is considered. A method for segmentation of the voice signal into periods of the fundamental tone, which is used for evaluation of the jitter and shimmer parameters, is proposed. A comparison of two ALS detectors was performed. The first detector was trained using voice features extracted by the proposed method, while the second detector was trained using features obtained with PRAAT toolkit. The result showed a significant improvement (by 20 %) in the accuracy of detecting ALS disease using the proposed method