arXiv:1809.10636
공식 코드 · Papers with Code
Conditional WaveGAN
원문 초록
Generative models are successfully used for image synthesis in the recent years. But when it comes to other modalities like audio, text etc little progress has been made. Recent works focus on generating audio from a generative model in an unsupervised setting. We explore the possibility of using generative models conditioned on class labels. Concatenation based conditioning and conditional scaling were explored in this work with various hyper-parameter tuning methods. In this paper we introduce Conditional WaveGANs (cWaveGAN). Find our implementation at https://github.com/acheketa/cwavegan
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BemaGANv2: A Tutorial and Comparative Survey of GAN-based Vocoders for Long-Term Audio Generation
XMAD-Bench: Cross-Domain Multilingual Audio Deepfake Benchmark
Conditional Diffusion Models with Classifier-Free Gibbs-like Guidance
Fast Text-to-Audio Generation with Adversarial Post-Training
OmniAudio: Generating Spatial Audio from 360-Degree Video
Detect All-Type Deepfake Audio: Wavelet Prompt Tuning for Enhanced Auditory Perception
토론
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