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Deep Harmonic Finesse: Signal Separation in Wearable Systems with Limited Data by Saffarpour, Mahya; Vali, Kourosh; Qian, Weitai; Kasap, Begum; Hedriana, Herman L.; Ghiasi, Soheil is a scholarly article available to read on EtoBox.

What is Deep Harmonic Finesse: Signal Separation in Wearable Systems with Limited Data about?

We present a method, referred to as Deep Harmonic Finesse (DHF), for separation of non-stationary quasi-periodic signals when limited data is available. The problem frequently arises in wearable systems in which, a combination of quasi-periodic physiological phenomena give rise to the sensed signal, and excessive data collection is prohibitive. Our approach utilizes prior knowledge of time-frequency patterns in the signals to mask and in-paint spectrograms. This is achieved through an application-inspired deep harmonic neural network coupled with an integrated pattern alignment component. The network's structure embeds the implicit harmonic priors within the time-frequency domain, while the pattern-alignment method transforms the sensed signal, ensuring a strong alignment with the network. The effectiveness of the algorithm is demonstrated in the context of non-invasive fetal monitoring using both synthesized and in vivo data. When applied to the synthesized data, our method exhibits significant improvements in signal-to-distortion ratio (26% on average) and mean squared error (80% on average), compared to the best competing method. When applied to in vivo data captured in pregnant

Author

Saffarpour, Mahya; Vali, Kourosh; Qian, Weitai; Kasap, Begum; Hedriana, Herman L.; Ghiasi, Soheil

Published

2024

Language

EN