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J Acoust Soc Am ; 151(4): 2773, 2022 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-35461490


Recognizing background information in human speech signals is a task that is extremely useful in a wide range of practical applications, and many articles on background sound classification have been published. It has not, however, been addressed with background embedded in real-world human speech signals. Thus, this work proposes a lightweight deep convolutional neural network (CNN) in conjunction with spectrograms for an efficient background sound classification with practical human speech signals. The proposed model classifies 11 different background sounds such as airplane, airport, babble, car, drone, exhibition, helicopter, restaurant, station, street, and train sounds embedded in human speech signals. The proposed deep CNN model consists of four convolution layers, four max-pooling layers, and one fully connected layer. The model is tested on human speech signals with varying signal-to-noise ratios (SNRs). Based on the results, the proposed deep CNN model utilizing spectrograms achieves an overall background sound classification accuracy of 95.2% using the human speech signals with a wide range of SNRs. It is also observed that the proposed model outperforms the benchmark models in terms of both accuracy and inference time when evaluated on edge computing devices.

Redes Neurais de Computação , Fala , Humanos , Som
Data Brief ; 41: 107977, 2022 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-35242951


The dataset contains low resolution thermal images corresponding to various sign language digits represented by hand and captured using the Omron D6T thermal camera. The resolution of the camera is 32 × 32 pixels. Because of the low resolution of the images captured by this camera, machine learning models for detecting and classifying sign language digits face additional challenges. Furthermore, the sensor's position and quality have a significant impact on the quality of the captured images. In addition, it is affected by external factors such as the temperature of the surface in comparison to the temperature of the hand. The dataset consists of 3200 images corresponding to ten sign digits, 0-9. Thus, each sign language digit consists of 320 images collected from different persons. The hand is oriented in various ways to capture all of the variations in the dataset.

Data Brief ; 42: 108037, 2022 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-35341036


An update to the previously published low resolution thermal imaging dataset is presented in this paper. The new dataset contains high resolution thermal images corresponding to various hand gestures captured using the FLIR Lepton 3.5 thermal camera and Purethermal 2 breakout board. The resolution of the camera is 160 × 120 with calibrated array of 19,200 pixels. The images captured by the thermal camera are light-independent. The dataset consists of 14,400 images with equal share from color and gray scale. The dataset consists of 10 different hand gestures. Each gesture has a total of 24 images from a single person with a total of 30 persons for the whole dataset. The dataset also contains the images captured under different orientations of the hand under different lighting conditions.

Artigo em Inglês | MEDLINE | ID: mdl-33755565


Lung ultrasound (US) imaging has the potential to be an effective point-of-care test for detection of COVID-19, due to its ease of operation with minimal personal protection equipment along with easy disinfection. The current state-of-the-art deep learning models for detection of COVID-19 are heavy models that may not be easy to deploy in commonly utilized mobile platforms in point-of-care testing. In this work, we develop a lightweight mobile friendly efficient deep learning model for detection of COVID-19 using lung US images. Three different classes including COVID-19, pneumonia, and healthy were included in this task. The developed network, named as Mini-COVIDNet, was bench-marked with other lightweight neural network models along with state-of-the-art heavy model. It was shown that the proposed network can achieve the highest accuracy of 83.2% and requires a training time of only 24 min. The proposed Mini-COVIDNet has 4.39 times less number of parameters in the network compared to its next best performing network and requires a memory of only 51.29 MB, making the point-of-care detection of COVID-19 using lung US imaging plausible on a mobile platform. Deployment of these lightweight networks on embedded platforms shows that the proposed Mini-COVIDNet is highly versatile and provides optimal performance in terms of being accurate as well as having latency in the same order as other lightweight networks. The developed lightweight models are available at

COVID-19/diagnóstico por imagem , Aprendizado Profundo , Interpretação de Imagem Assistida por Computador/métodos , Sistemas Automatizados de Assistência Junto ao Leito , Ultrassonografia/métodos , Humanos , SARS-CoV-2
IEEE Trans Neural Netw Learn Syst ; 32(3): 932-946, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33544680


Chest computed tomography (CT) imaging has become indispensable for staging and managing coronavirus disease 2019 (COVID-19), and current evaluation of anomalies/abnormalities associated with COVID-19 has been performed majorly by the visual score. The development of automated methods for quantifying COVID-19 abnormalities in these CT images is invaluable to clinicians. The hallmark of COVID-19 in chest CT images is the presence of ground-glass opacities in the lung region, which are tedious to segment manually. We propose anamorphic depth embedding-based lightweight CNN, called Anam-Net, to segment anomalies in COVID-19 chest CT images. The proposed Anam-Net has 7.8 times fewer parameters compared to the state-of-the-art UNet (or its variants), making it lightweight capable of providing inferences in mobile or resource constraint (point-of-care) platforms. The results from chest CT images (test cases) across different experiments showed that the proposed method could provide good Dice similarity scores for abnormal and normal regions in the lung. We have benchmarked Anam-Net with other state-of-the-art architectures, such as ENet, LEDNet, UNet++, SegNet, Attention UNet, and DeepLabV3+. The proposed Anam-Net was also deployed on embedded systems, such as Raspberry Pi 4, NVIDIA Jetson Xavier, and mobile-based Android application (CovSeg) embedded with Anam-Net to demonstrate its suitability for point-of-care platforms. The generated codes, models, and the mobile application are available for enthusiastic users at

COVID-19/diagnóstico por imagem , Aprendizado Profundo , Processamento de Imagem Assistida por Computador/métodos , Pulmão/diagnóstico por imagem , Redes Neurais de Computação , Tomografia Computadorizada por Raios X/métodos , COVID-19/epidemiologia , Humanos