TY - JOUR
T1 - Optimizing MFCC parameters for the automatic detection of respiratory diseases
AU - Yan, Yuyang
AU - Simons, Sami O.
AU - van Bemmel, Loes
AU - Reinders, Lauren G.
AU - Franssen, Frits M.E.
AU - Urovi, Visara
N1 - Funding Information:
The work was supported by NWO Aspasia grant (no: 91716421), DACIL project (KICH1.GZ03.21.023), and the SPEAK-study (7.3.22.013) sponsored by the Lung Foundation Netherlands. The authors would like to thank the Cambridge University for sharing the data collected via the COVID-19 app, the Indian Institute of Science Bangalore for opening the Coswara dataset, the Saarland University who collected the Saarbr\u00FCcken Voice Disorders (SVD) database, and the TACTICAS study for the data collection of TACTICAS dataset.
Funding Information:
The work was supported by NWO Aspasia grant (no: 91716421), DACIL project (KICH1.GZ03.21.023) and the SPEAK-study. The authors would like to thank the Cambridge University for sharing the data collected via the COVID-19 app, the Indian Institute of Science Bangalore for opening the Coswara dataset, the Saarland University who collected the Saarbr\u00FCcken Voice Disorders (SVD) database, and the TACTICAS study for the data collection of TACTICAS dataset.
Publisher Copyright:
© 2024 The Author(s)
PY - 2025/1/15
Y1 - 2025/1/15
N2 - Voice signals originating from the respiratory tract are utilized as valuable acoustic biomarkers for the diagnosis and assessment of respiratory diseases. Among the employed acoustic features, Mel Frequency Cepstral Coefficients (MFCC) are widely used for automatic analysis, with MFCC extraction commonly relying on default parameters. However, no comprehensive study has systematically investigated the impact of MFCC extraction parameters on respiratory disease diagnosis. In this study, we address this gap by examining the effects of key parameters, namely the number of coefficients, frame length, and hop length between frames, on respiratory condition examination. Our investigation uses four datasets: the Cambridge COVID-19 Sound database, the Coswara dataset, the Saarbrücken Voice Disorders (SVD) database, and a TACTICAS dataset. The Support Vector Machine (SVM) is employed as the classifier, given its widespread adoption and efficacy. Our findings indicate that the accuracy of MFCC decreases as hop length increases, and the optimal number of coefficients is observed to be approximately 30. The performance of MFCC varies with frame length across the datasets: for the COVID-19 datasets (Cambridge COVID-19 Sound database and Coswara dataset), performance declines with longer frame lengths, while for the SVD dataset, performance improves with increasing frame length (from 50 ms to 500 ms). Furthermore, we investigate the optimized combination of these parameters and observe substantial enhancements in accuracy. Compared to the worst combination, the SVM model achieves an accuracy of 81.1%, 80.6%, and 71.7%, with improvements of 19.6%, 16.10%, and 14.90% for the Cambridge COVID-19 Sound database, the Coswara dataset, and the SVD dataset respectively. To validate the generalization of these findings, we employ the Long Short-Term Memory (LSTM) model as a validation model. Remarkably, the LSTM model also demonstrates improved accuracy of 14.12%, 10.10%, and 6.68% across the datasets when utilizing the optimal combination of parameters. The optimal parameters are validated using an external voice pathology dataset (TACTICAS dataset). The results demonstrate the generalization capabilities of the optimized parameters across various pathologies, machine-learning models, and languages.
AB - Voice signals originating from the respiratory tract are utilized as valuable acoustic biomarkers for the diagnosis and assessment of respiratory diseases. Among the employed acoustic features, Mel Frequency Cepstral Coefficients (MFCC) are widely used for automatic analysis, with MFCC extraction commonly relying on default parameters. However, no comprehensive study has systematically investigated the impact of MFCC extraction parameters on respiratory disease diagnosis. In this study, we address this gap by examining the effects of key parameters, namely the number of coefficients, frame length, and hop length between frames, on respiratory condition examination. Our investigation uses four datasets: the Cambridge COVID-19 Sound database, the Coswara dataset, the Saarbrücken Voice Disorders (SVD) database, and a TACTICAS dataset. The Support Vector Machine (SVM) is employed as the classifier, given its widespread adoption and efficacy. Our findings indicate that the accuracy of MFCC decreases as hop length increases, and the optimal number of coefficients is observed to be approximately 30. The performance of MFCC varies with frame length across the datasets: for the COVID-19 datasets (Cambridge COVID-19 Sound database and Coswara dataset), performance declines with longer frame lengths, while for the SVD dataset, performance improves with increasing frame length (from 50 ms to 500 ms). Furthermore, we investigate the optimized combination of these parameters and observe substantial enhancements in accuracy. Compared to the worst combination, the SVM model achieves an accuracy of 81.1%, 80.6%, and 71.7%, with improvements of 19.6%, 16.10%, and 14.90% for the Cambridge COVID-19 Sound database, the Coswara dataset, and the SVD dataset respectively. To validate the generalization of these findings, we employ the Long Short-Term Memory (LSTM) model as a validation model. Remarkably, the LSTM model also demonstrates improved accuracy of 14.12%, 10.10%, and 6.68% across the datasets when utilizing the optimal combination of parameters. The optimal parameters are validated using an external voice pathology dataset (TACTICAS dataset). The results demonstrate the generalization capabilities of the optimized parameters across various pathologies, machine-learning models, and languages.
KW - Acoustic biomarkers
KW - MFCC extraction parameters
KW - Optimized parameters
KW - Respiratory disease
U2 - 10.1016/j.apacoust.2024.110299
DO - 10.1016/j.apacoust.2024.110299
M3 - Article
SN - 0003-682X
VL - 228
JO - Applied Acoustics
JF - Applied Acoustics
M1 - 110299
ER -