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Methods/Phase Shuffle

Phase Shuffle

AudioIntroduced 200030 papers
Source Paper

Description

Phase Shuffle is a technique for removing pitched noise artifacts that come from using transposed convolutions in audio generation models. Phase shuffle is an operation with hyperparameter nnn. It randomly perturbs the phase of each layer’s activations by −nnn to nnn samples before input to the next layer.

In the original application in WaveGAN, the authors only apply phase shuffle to the discriminator, as the latent vector already provides the generator a mechanism to manipulate the phase of a resultant waveform. Intuitively speaking, phase shuffle makes the discriminator’s job more challenging by requiring invariance to the phase of the input waveform.

Papers Using This Method

NAIST Simultaneous Speech Translation System for IWSLT 20242024-06-30(Un)paired signal-to-signal translation with 1D conditional GANs2024-03-05The Effects of Signal-to-Noise Ratio on Generative Adversarial Networks Applied to Marine Bioacoustic Data2023-12-22Framewise WaveGAN: High Speed Adversarial Vocoder in Time Domain with Very Low Computational Complexity2022-12-08HiFi-WaveGAN: Generative Adversarial Network with Auxiliary Spectrogram-Phase Loss for High-Fidelity Singing Voice Generation2022-10-23WaveGAN: Frequency-aware GAN for High-Fidelity Few-shot Image Generation2022-07-15WOLONet: Wave Outlooker for Efficient and High Fidelity Speech Synthesis2022-06-20NatiQ: An End-to-end Text-to-Speech System for Arabic2022-06-15Unified Source-Filter GAN with Harmonic-plus-Noise Source Excitation Generation2022-05-12MSR-NV: Neural Vocoder Using Multiple Sampling Rates2021-09-28StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion2021-07-21Digital Einstein Experience: Fast Text-to-Speech for Conversational AI2021-07-21Interpreting intermediate convolutional layers of generative CNNs trained on waveforms2021-04-19Unified Source-Filter GAN: Unified Source-filter Network Based On Factorization of Quasi-Periodic Parallel WaveGAN2021-04-10Adversarial Attacks and Defenses for Speech Recognition Systems2021-03-31Improve GAN-based Neural Vocoder using Pointwise Relativistic LeastSquare GAN2021-03-26LVCNet: Efficient Condition-Dependent Modeling Network for Waveform Generation2021-02-22Study of Pre-processing Defenses against Adversarial Attacks on State-of-the-art Speaker Recognition Systems2021-01-22Synthesising Realistic Calcium Imaging Data of Neuronal Populations Using GAN2021-01-01StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalization2020-11-03