11 citations · 20 across the 12 of their papers we have counts for
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cs.SD2023
Remixed2Remixed: Domain adaptation for speech enhancement by Noise2Noise learning with Remixing
Li Li, Shogo Seki
This paper proposes Remixed2Remixed, a domain adaptation method for speech enhancement, which adopts Noise2Noise (N2N) learning to adapt models trained on artificially generated (o…
cs.SD2023
iSTFTNet2: Faster and More Lightweight iSTFT-Based Neural Vocoder Using 1D-2D CNN
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
The inverse short-time Fourier transform network (iSTFTNet) has garnered attention owing to its fast, lightweight, and high-fidelity speech synthesis. It obtains these characterist…
cs.SD2023
Wave-U-Net Discriminator: Fast and Lightweight Discriminator for Generative Adversarial Network-Based Speech Synthesis
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
In speech synthesis, a generative adversarial network (GAN), training a generator (speech synthesizer) and a discriminator in a min-max game, is widely used to improve speech quali…