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Adversarial loss in a conditional generative adversarial network (GAN) is not designed to directly optimize evaluation metrics of a target task, and thus, may not always guide the generator in a GAN to generate data with improved metric scores.
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Error modeling via asymmetric laplace distribution for deep neural network based single-channel speech enhancement
Chai, L., Du, J., and Lee, C.-H · 2018
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Exploring speech enhancement with generative adversarial networks for robust speech recognition
Donahue, C., Li, B., and Prabhavalkar, R · 2018
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Speech denoising with deep feature losses
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On adversarial training and loss functions for speech enhancement
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Time-frequency masking-based speech enhancement using generative adversarial network
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Esrgan: Enhanced super-resolution generative adversarial networks
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Sergan: Speech enhancement using relativistic generative adversarial networks with gradient penalty
Baby, D. and Verhulst, S · 2019
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Phase-aware speech enhancement with deep complex u-net
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Fu, S.-W., Liao, C.-F., and Tsao, Y · 2019
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