{
  "id": "1901.00295",
  "version": "v1",
  "published": "2019-01-02T08:39:05.000Z",
  "updated": "2019-01-02T08:39:05.000Z",
  "title": "End-to-End Model for Speech Enhancement by Consistent Spectrogram Masking",
  "authors": [
    "Xingjian Du",
    "Mengyao Zhu",
    "Xuan Shi",
    "Xinpeng Zhang",
    "Wen Zhang",
    "Jingdong Chen"
  ],
  "categories": [
    "cs.SD",
    "cs.AI",
    "cs.MM",
    "eess.AS"
  ],
  "abstract": "Recently, phase processing is attracting increasinginterest in speech enhancement community. Some researchersintegrate phase estimations module into speech enhancementmodels by using complex-valued short-time Fourier transform(STFT) spectrogram based training targets, e.g. Complex RatioMask (cRM) [1]. However, masking on spectrogram would violentits consistency constraints. In this work, we prove that theinconsistent problem enlarges the solution space of the speechenhancement model and causes unintended artifacts. ConsistencySpectrogram Masking (CSM) is proposed to estimate the complexspectrogram of a signal with the consistency constraint in asimple but not trivial way. The experiments comparing ourCSM based end-to-end model with other methods are conductedto confirm that the CSM accelerate the model training andhave significant improvements in speech quality. From ourexperimental results, we assured that our method could enha",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-01-02T08:39:05.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "end-to-end model",
      "consistent spectrogram masking",
      "speech enhancement",
      "researchersintegrate phase estimations module",
      "model training andhave significant improvements"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}