Separation of Real Time Heart Sound Signal from Lung Sound Signal Using Neural Network

Lung disease recognition methods using audio-based analysis with machine learning

“…While DL techniques offer ideal capabilities for feature extraction during HLS separation, the diverse nature of HLS signals can undermine the reliability of neural networks. Lin et al [ 142 ] NMF-based constant Q transform for dimensionality reduction Shah et al [ 143 ] An unsupervised BSS and enhanced NMF with shared factors and method that does not require any training data Canadas et al [ 144 ] A spectrotemporal clustering by NMF Montoro et al [ 145 ] A parallel source partitioning system derived from NMF Sathesh et al [ 146 ] HS real-time signals from LS signals with ANN Tsai et al [ 26 ] A periodical deep autoencoder encoded method …”

The Acquisition and Short Time Fourier Transform of Lung Sounds

“…The short-time Fourier transform overcomes the shortcomings of the traditional Fourier transform requiring for frequency distribution of lung sound signals over the entire time period, and reduces the stability requirements for lung sound signals, so the short-time Fourier transform can be used in dynamically frequency-domain research on Lung Sounds, lung sound signals in short time Fourier transform spectrum diagram as shown in figure 5. Lung sound signals time-frequency spectrum diagram reaction lung sound signals energy spatial distribution, the change of the spatial energy distribution means that the change features of lung sound signals, so the signal energy of the different frequency components can reflect the different measured signals characteristics of lung sounds, the use of short-time Fourier transform to extract characteristic changes in lung sound signals, which can determine whether the lung sounds normal and can get lung disease classification results [8][9][10][11].…”