Applied Weighted Parameters Approach for Noise Removal in Audio Processing Environment

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ID: 313050
2024
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Abstract
In the world of artificial intelligence and speech technology, it's becoming increasingly crucial to improve how we filter out background noise from audio, aiming for efficiency without unnecessary complexity. So, the challenge is to come up with a really effective algorithm for real-time noise reduction, ensuring optimal performance. In this study, we've delved into a deep learning approach using a convolutional neural network (CNN) to tackle noise in audio signals. We trained our model on a substantial dataset named "Edinburgh DataShare". Throughout the development of the CNN model, we incorporated Softmax and rectified linear unit as an activation functions, along with the ADAM optimization algorithm. To model evaluation, the model over 50 epochs showed a really low loss of 0.012. Hence, our findings affirm that the CNN network performs well in effectively mitigating noise from audio signals.
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imported_1777058703_69ebc38fd2c22 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Aleena Mumtaz, Sajid Ali, Ghulam Irtaza, Muhammad Hassan Raza, Saif Ur Rehman Khan, Muhammad Muzamil Aslam
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
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URL
https://jcbi.org/index.php/Main/article/view/442
Keywords Keywords not found

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