Soft Spoken Murmur Analysis Using Novel Random Forest Algorithm Compared with Convolutional Neural Network for Improving Accuracy

D. Reddy, T. Kumar

2023

Abstract

This research aimed to enhance the accuracy of converting subtle murmurs into clear speech. The study employed an advanced Random Forest algorithm, comparing its efficacy to that of a Convolutional Neural Network (CNN). Both methods were applied to two distinct sets, each comprising 20 samples. Prior to testing, a G-power score of 80% and a confidence interval of 95% were set. Results indicated that the Random Forest method achieved 99.86% accuracy, while the CNN obtained 95.89%. A significant difference in performance between the two was evident, supported by a p-value of 0.001. Hence, the Random Forest algorithm proved more efficient than the CNN in transforming soft murmurs to clear speech.

Paper Citation

in Harvard Style

Reddy D. and Kumar T. (2023). Soft Spoken Murmur Analysis Using Novel Random Forest Algorithm Compared with Convolutional Neural Network for Improving Accuracy. In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 601-607. DOI: 10.5220/0012603000003739

in Bibtex Style

@conference{ai4iot23,
author={D. Reddy and T. Kumar},
title={Soft Spoken Murmur Analysis Using Novel Random Forest Algorithm Compared with Convolutional Neural Network for Improving Accuracy},
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={601-607},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012603000003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Soft Spoken Murmur Analysis Using Novel Random Forest Algorithm Compared with Convolutional Neural Network for Improving Accuracy
SN - 978-989-758-661-3
AU - Reddy D.
AU - Kumar T.
PY - 2023
SP - 601
EP - 607
DO - 10.5220/0012603000003739
PB - SciTePress