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- W3208993597 abstract "Processing of physiological signals contributes significantly in identifying the respiratory problems. One of the fundamental means of data augmentation is noise addition. However, a widely recognized strategy does not exist for learning the deep learning model. This research work examines the effectiveness of augmented data set using pink noise in classifying the respiratory sounds with three pre-trained architectures. The noise levels are added in sequential random manner to the lung sound signals and wavelet coherence metric is utilized to validate the similarity between the original and generated signals. The augmented time domain signals are transformed into time -frequency images namely spectrograms, Mel Spectrograms and scalograms. These transformed images are resized and experimented using different pre-trained models viz. AlexNet, GoogLeNet and ResNet for categorizing the normal and abnormal lung sounds. The result indicates that pink noise addition provides better classification accuracy of 94.7 % with scalogram type of representation when classified with ResNet classifier compared to other classifiers and other noises considered for study in literature." @default.
- W3208993597 created "2021-11-08" @default.
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- W3208993597 date "2021-08-27" @default.
- W3208993597 modified "2023-09-24" @default.
- W3208993597 title "Prediction Model based on pink noise respiratory signal augmentation" @default.
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- W3208993597 doi "https://doi.org/10.1109/mascon51689.2021.9563441" @default.
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