Cough sounds as a descriptor have been used for detecting various respiratory ailments based on its intensity, duration of intermediate phase between two cough sounds, repetitions, dryness etc. However, COVID-19 diagnosis using only cough sounds is challenging because of cough being a common symptom among many non COVID-19 health diseases and inherent data imbalance within the available datasets. As one of the approach in this direction, we explore the robustness of multi-domain representation by performing the early fusion over a wide set of temporal, spectral and tempo-spectral handcrafted features, followed by training a Support Vector Machine (SVM) classifier. In our second approach, using a contrastive loss function we learn a latent space from Mel Filter Cepstral Coefficients (MFCCs) where representations belonging to samples having similar cough characteristics are closer. This helps learn representations for the highly varied COVIDnegative class (healthy and symptomatic COVID-negative), by learning multiple smaller clusters. Using only the DiCOVA data, multi-domain features yields an absolute improvement of 0.74% and 1.07%, whereas our second approach shows an improvement of 2.09% and 3.98%, over the blind test and validation set, respectively, when compared with challenge baseline.
Lung Auscultation is a non-invasive process of distinguishing normal respiratory sounds from abnormal ones by analyzing the airflow along the respiratory tract. With developments in the Deep Learning (DL) techniques and wider access to anonymized medical data, automatic detection of specific sounds such as crackles and wheezes have been gaining popularity. In this paper, we propose to use two sets of diversified acoustic biomarkers extracted using Discrete Wavelet Transform (DWT) and deep encoded features from the intermediate layer of a pre-trained Audio Event Detection (AED) model trained using sounds from daily activities. First set of biomarkers highlight the time frequency localization characteristics obtained from DWT coefficients. However, the second set of deep encoded biomarkers captures a generalized reliable representation, and thus indemnifies the scarcity of training samples and the class imbalance in dataset. The model trained using these features achieves a 15.05% increase in terms of the specificity over the baseline model that uses spectrogram features. Moreover, ensemble of DWT features and deep encoded feature based models show absolute improvements of 8.32%, 6.66% and 7.40% in terms of sensitivity, specificity and ICBHIscore, respectively, and clearly outperforms the state-of-the-art with a significant margin.
There are several Iris recognition0 techniques. But method proposed by Daugman is considered to be most efficient technique for IRIS segmentation and feature extraction. Recent studies have shown that there is better classifier which when properly trained with sufficient numbers of features are better than the hamming distance based classifier. But more number of features increases the computational complexity due to the need for feature optimization by kernel based classifiers. Hence in this work we propose a unique technique of first extracting huge numbers of features from the IRIS images and then reducing the features by using PCA based linear dimensionality reduction technique. We first segment the IRIS images with a technique proposed by Daugman, further Gabor features are extracted from the segmented IRIS image. These features are reduced using feature reduction technique. The features are classified using Multiclass support vector machine. We show that the accuracy of the IRIS recognition technique is very high using this method.
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