{
  "annotation_id": "94507c3e-2d14-4f8c-9f5c-bf2f0085c619",
  "date_created": "2026-02-17T05:53:23.069000Z",
  "date_modified": "2026-02-17T05:53:23.069000Z",
  "file_hash": "5bfc31ae822f9bdf987a4b04b0a6dac35f395f0535b8bb0c36b7b0ba04428142",
  "private": false,
  "record": {
    "abstract": "This paper addresses $\\textit{unsupervised}$ diffusion-based single-channel speech enhancement (SE). Prior work in this direction combines a score-based diffusion model trained on clean speech with a Gaussian noise model whose covariance is structured by non-negative matrix factorization (NMF). This combination is used within an iterative expectation-maximization (EM) scheme, in which a diffusion-based posterior-sampling E-step estimates the clean speech. We first revisit this framework and propose to explicitly model both speech and acoustic noise as latent variables, jointly sampling them in the E-step instead of sampling speech alone as in previous approaches. We then introduce a new unsupervised SE framework that replaces the NMF noise prior with a diffusion-based noise model, learned jointly with the speech prior in a single conditional score model. Within this framework, we derive two variants: one that implicitly accounts for noise and one that explicitly treats noise as a latent variable. Experiments on WSJ0-QUT and VoiceBank-DEMAND show that explicit noise modeling systematically improves SE performance for both NMF-based and diffusion-based noise priors. Under matched conditions, the diffusion-based noise model attains the best overall quality and intelligibility among unsupervised methods, while under mismatched conditions the proposed NMF-based explicit-noise framework is more robust and suffers less degradation than several supervised baselines. Our code will be publicly available on this $\\href{https://github.com/jeaneudesAyilo/enudiffuse}{URL}$.",
    "arxiv_id": "2601.09931",
    "authors": [
      "Jean-Eudes Ayilo",
      "Mostafa Sadeghi",
      "Romain Serizel",
      "Xavier Alameda-Pineda"
    ],
    "categories": [
      "cs.SD"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Diffusion-based Frameworks for Unsupervised Speech Enhancement",
    "url": "https://arxiv.org/abs/2601.09931",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "45fdfe5a-62dc-4089-9562-3d32fb3fac3b",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}