Research Article
A Comparative Analysis of Various Deep-Learning Models for Noise Suppression
@ARTICLE{10.4108/eetiot.4502, author={Henil Gajjar and Trushti Selarka and Absar M. Lakdawala and Dhaval B. Shah and P. N. Kapil}, title={A Comparative Analysis of Various Deep-Learning Models for Noise Suppression}, journal={EAI Endorsed Transactions on Internet of Things}, volume={10}, number={1}, publisher={EAI}, journal_a={IOT}, year={2023}, month={11}, keywords={CNN, Noise Suppression, Neural Networks, Audio Processing}, doi={10.4108/eetiot.4502} }- Henil Gajjar
Trushti Selarka
Absar M. Lakdawala
Dhaval B. Shah
P. N. Kapil
Year: 2023
A Comparative Analysis of Various Deep-Learning Models for Noise Suppression
IOT
EAI
DOI: 10.4108/eetiot.4502
Abstract
Excessive noise in speech communication systems is a major issue affecting various fields, including teleconferencing and hearing aid systems. To tackle this issue, various deep-learning models have been proposed, with autoencoder-based models showing remarkable results. In this paper, we present a comparative analysis of four different deep learning based autoencoder models, namely model ‘alpha’, model ‘beta’, model ‘gamma’, and model ‘delta’ for noise suppression in speech signals. The performance of each model was evaluated using objective metric, mean squared error (MSE). Our experimental results showed that the model ‘alpha’ outperformed the other models, achieving a minimum error of 0.0086 and maximum error of 0.0158. The model ‘gamma’ also performed well, with a minimum error of 0.0169 and maximum error of 0.0216. These findings suggest that the pro-posed models have great potential for enhancing speech communication systems in various fields.
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