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Diffsound (discrete diffusion for audio) is a method in generative audio modeling that applies discrete diffusion processes to create or transform audio data. It is part of a broader class of diffusion-based generative models adapted specifically for the discrete, sequential nature of audio signals.
WaveNet is a deep generative model for raw audio waveforms developed by DeepMind, known for producing highly realistic speech and audio synthesis through neural network architectures.
AudioLM is a neural network-based audio language model designed to generate coherent and high-quality audio sequences by learning from raw audio data. It leverages techniques from natural language processing and audio signal processing to produce extended audio continuations without explicit semantic conditioning.
HiFi-GAN is a deep learning-based neural vocoder designed for high-fidelity speech synthesis. It uses generative adversarial networks to efficiently produce natural-sounding audio waveforms from mel-spectrograms.
MelNet is a deep learning model designed for generating mel-spectrograms, which are visual representations of audio signals. It utilizes a probabilistic hierarchical approach to model complex audio structures, enabling applications in speech synthesis and audio generation. MelNet advances the state of the art in audio generation by capturing long-term dependencies and rich spectral details.