Neural network-based approaches inspired from other areas such as computer vision [10, 146, 188]
and generative adversarial networks [142, 321, 469, 596] or developed for general audio processing
tasks [157, 588] have outperformed the classical approaches. Various neural network models
based on different architectures, including fully connected neural networks [606], deep denoising
autoencoder [346], CNN [143], LSTM [77], and Transformer [263] have effectively handled diverse
noisy conditions.
AReviewofDeepLearningTechniquesforSpeechProcessing