Phase sensitive masking-based single channel speech enhancement using conditional generative adversarial network
We propose PSMGAN, an efficient phase sensitive masking-based single-channel speech
enhancement technique using a conditional generative adversarial network (cGAN). The
time–frequency (TF) masking-based speech enhancement approaches through deep neural
networks (DNNs) have shown large speech intelligibility improvements. However, these
approaches fail to achieve better enhancement results at low signal-to-noise ratio (SNR)
conditions since they ignore the phase information during reconstruction. Alternatively …
enhancement technique using a conditional generative adversarial network (cGAN). The
time–frequency (TF) masking-based speech enhancement approaches through deep neural
networks (DNNs) have shown large speech intelligibility improvements. However, these
approaches fail to achieve better enhancement results at low signal-to-noise ratio (SNR)
conditions since they ignore the phase information during reconstruction. Alternatively …
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