Fundamentals of Noise Reduction

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Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition (2017, methodology): “They are crucial components, either explicitly [3] or implicitly [4, 5], in ASR systems for noise robustness.”
Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks (2017, background): “components), which is then used to suppress noise components in the frequency domain to obtain a clean speech spectrum from a noisy speech one [13]–[17].”
Deep Learning for Audio Signal Processing (2019, methodology): “Besides conventional enhancement techniques [129], deep neural networks have been widely adopted to either directly reconstruct clean speech [132], [133] or estimate masks [134]–[136] from”
End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks (2017, methodology): “we also reported the average segmental SNR improvement (SSNRI) [68], STOI and PESQ by different enhancement models and oracle “ideal binary mask”(IBM) [69] (simply as a refer-”