title: EDMSound: Spectrogram Based Diffusion Models for Efficient and
    High-Quality Audio Synthesis
summary: Audio diffusion models can synthesize a wide variety of sounds. Existing
  models often operate on the latent domain with cascaded phase recovery modules
  to reconstruct waveform. This poses challenges when generating high-fidelity
  audio. In this paper, we propose EDMSound, a diffusion-based generative model
  in spectrogram domain under the framework of elucidated diffusion models (EDM).
  Combining with efficient deterministic sampler, we achieved similar Fr\'echet
  audio distance (FAD) score as top-ranked baseline with only 10 steps and
  reached state-of-the-art performance with 50 steps on the DCASE2023 foley sound
  generation benchmark. We also revealed a potential concern regarding diffusion
  based audio generation models that they tend to generate samples with high
  perceptual similarity to the data from training data. Project page:
  https://agentcooper2002.github.io/EDMSound/
paper_authors._id: 6555b2959dc61e22c51e2c09
paper_authors.name: Ge Zhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-16T15:51:14.761Z
paper_authors._id: 6526f16b00040a5395b82c96
paper_authors.avatarUrl: /avatars/592366207bc919a6984b3a0d816314c7.svg
paper_authors.fullname: Ge Zhu
paper_authors.type: user
paper_authors.user: gzhu06
paper_authors._id: 6555b2959dc61e22c51e2c0a
paper_authors.name: Yutong Wen
paper_authors._id: 6555b2959dc61e22c51e2c0b
paper_authors.name: Marc-André Carbonneau
paper_authors._id: 6555b2959dc61e22c51e2c0c
paper_authors.name: Zhiyao Duan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-17T07:41:41.267Z
paper_authors._id: 6556f3c3ee35f7d8bcf7706d
paper_authors.avatarUrl: /avatars/423682fbf354f085232237cbf0f51bbe.svg
paper_authors.fullname: Zhiyao Duan
paper_authors.type: user
paper_authors.user: zhiyaoduan
paper_title: EDMSound: Spectrogram Based Diffusion Models for Efficient and
    High-Quality Audio Synthesis
paper_summary: Audio diffusion models can synthesize a wide variety of sounds. Existing
  models often operate on the latent domain with cascaded phase recovery modules
  to reconstruct waveform. This poses challenges when generating high-fidelity
  audio. In this paper, we propose EDMSound, a diffusion-based generative model
  in spectrogram domain under the framework of elucidated diffusion models (EDM).
  Combining with efficient deterministic sampler, we achieved similar Fr\'echet
  audio distance (FAD) score as top-ranked baseline with only 10 steps and
  reached state-of-the-art performance with 50 steps on the DCASE2023 foley sound
  generation benchmark. We also revealed a potential concern regarding diffusion
  based audio generation models that they tend to generate samples with high
  perceptual similarity to the data from training data. Project page:
  https://agentcooper2002.github.io/EDMSound/
paper_ai_keywords: diffusion models latent domain phase recovery modules spectrogram domain elucidated diffusion models (EDM) deterministic sampler Fréchet audio distance (FAD) high-fidelity audio DCASE2023 foley sound generation benchmark perceptual similarity