Speech enhancement with noise estimation and filtration using deep learning models

Speech enhancement helps in eliminating the environmental noises from the communica-tion signals. The main intention of the augmentation system is to develop the perceptual quality of communication or speech. For this purpose, various filtering schemes, spectral restoration models and speech models were implemented. In order to improve the odds of reducing noise and restoring the original signal, artificial intelligence (AI) and machine learning algorithms (MLA) were included into every sector. Deep transfer learning was used in this work to remove noise from the data and restore the original signals. This proposed approach includes a filtration scheme instead of using a convolution layer in the RESNET-50 architecture. The filters tested for speech enhanced deep learning models are modified Kalman filter and enhanced wiener filter. The performance metrics were calculated be-tween various algorithms and proposed models to identify which approaches to follow the better way result obtained. The performance metrics compared PESA, LSD and segSNR for different low signal to noise ratio conditions. (c) 2022 Elsevier B.V. All rights reserved.