Bacterial Acoustic Fingerprinting: Nanomotion and Spectral Feature Analysis between Real and Synthetic Oscillations Using Deep Neural Networks
Subject Areas : Journal of Chemical Health Risks
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Keywords: Bacterial vibration, E. coli , Nanomotion, Original sound, Synthetic sound,
Abstract :
Background: Researchers are becoming interested in the use of bacterial acoustic vibrations to classify and diagnose microbes. In the past, biochemical analysis and microscopy were used in microbiology processed with Fast Fourier Transform and extracted using Mel-Frequency Cepstral Coefficients and finally classified by using Convolutional Neural Networks and Long Short-Term Memory networks. Synthetic sound samples for bacteria were made with GANs and confirmed by comparing their spectra. Results: It was observed that the vibrational patterns in live bacteria differed greatly from those of AI-generated sounds, with signals from real bacteria showing greater variety of frequencies and more variability. The artificial bacterial sounds captured the vibrations effectively apart from some discrepancies in the energy at low frequencies and the presence of harmonics. The accuracy of both models (>94%) demonstrates that sound-based identification of bacteria can be successful. Conclusions: This research points out that bacterial acoustic signatures can be used for fast and noninvasive diagnosis and continuous monitoring of bacteria.
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