Improved expectation-maximization framework for speech enhancement based on iterative noise estimation

Tingtian Li, Pak Kong Lun, Tak Wai Shen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


Recently, our team developed a novel Expectation Maximization (EM) framework for speech enhancement. It gives a significantly improved estimation of the speech power spectrum that outperforms many traditional approaches. In this paper, we further extend the EM framework by including an efficient iterative noise estimation algorithm, which improves the estimation of the noise power spectrum from the noisy observation. Besides, we notice that some speech frames, particularly those with high signal to noise ratio (SNR), need to be monitored closely during the iterative enhancement process, or spectral distortion may result. A stopping criterion is thus developed to stop the iteration when a good result has been achieved. Experimental results show that the new approach gives a significant improvement over the original EM framework and also traditional speech enhancement methods.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Digital Signal Processing, DSP 2015
Number of pages5
ISBN (Electronic)9781479980581
Publication statusPublished - 9 Sep 2015
EventIEEE International Conference on Digital Signal Processing, DSP 2015 - Singapore, Singapore
Duration: 21 Jul 201524 Jul 2015


ConferenceIEEE International Conference on Digital Signal Processing, DSP 2015


  • EM algorithm
  • iterative regularization
  • noise power spectral density estimation
  • Speech enhancement

ASJC Scopus subject areas

  • Signal Processing

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