Analysis of Body Signals Solid Food Swallowing Sounds To Analyse Body Signals

This report is about the analysis of solid food swallowing sound signals. The primary aim for the report is to record the sound from microphone and being familiar with the potential noise sources and then applying different techniques to analyses body signals. In order to perform the time domain and frequency domain data analysis, two data sets are compared. One data set (Dataset_1) is provided and the other dataset(Dataset_2) is obtained by recording 5 different samples of sound files(.wav) through mobile phone microphone. The signal processing methods includes show of time-domain signals, quantification of signal size and signal-to-noise ratios (SNR), and investigation of how down sampling affects these two metrics. We also investigated the frequency spectra to estimate the essential bandwidth of swallowing events and examine how down sampling influences these bandwidths. Finally, we compare all metrics between Dataset_1 and Dataset_2, presenting graphs and tables with key findings

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-11
DOI
https://doi.org/10.5281/zenodo.17327891
Primary Topic
Antibiotic Resistance in Bacteria
Type
article
Field-Weighted Citation Impact
0.00
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article

Analysis of Body Signals Solid Food Swallowing Sounds To Analyse Body Signals

Zenodo (CERN European Organization for Nuclear Research)
Antibiotic Resistance in Bacteria
article

Analysis of Body Signals Solid Food Swallowing Sounds To Analyse Body Signals

article en

Abstract

This report is about the analysis of solid food swallowing sound signals. The primary aim for the report is to record the sound from microphone and being familiar with the potential noise sources and then applying different techniques to analyses body signals. In order to perform the time domain and frequency domain data analysis, two data sets are compared. One data set (Dataset_1) is provided and the other dataset(Dataset_2) is obtained by recording 5 different samples of sound files(.wav) through mobile phone microphone. The signal processing methods includes show of time-domain signals, quantification of signal size and signal-to-noise ratios (SNR), and investigation of how down sampling affects these two metrics. We also investigated the frequency spectra to estimate the essential bandwidth of swallowing events and examine how down sampling influences these bandwidths. Finally, we compare all metrics between Dataset_1 and Dataset_2, presenting graphs and tables with key findings

Zenodo (CERN European Organization for Nuclear Research)
Zero hunger
Openalex Percentile: Top 55%
Antibiotic Resistance in Bacteria
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