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Audio diffusion models can synthesize a wide variety of sounds. Existingmodels often operate on the latent domain with cascaded phase recovery modulesto reconstruct waveform. This poses challenges when generating high-fidelityaudio. In this paper, we propose EDMSound, a diffusion-based generative modelin spectrogram domain under the framework of elucidated diffusion models (EDM).Combining with efficient deterministic sampler, we achieved similar Fr\'echetaudio distance (FAD) score as top-ranked baseline with only 10 steps andreached state-of-the-art performance with 50 steps on the DCASE2023 foley soundgeneration benchmark. We also revealed a potential concern regarding diffusionbased audio generation models that they tend to generate samples with highperceptual similarity to the data from training data. Project page:https://agentcooper2002.github.io/EDMSound/

EDMSound: Spectrogram Based Diffusion Models for Efficient and High-Quality Audio Synthesis