Maximum Likelihood Estimation of the Interference-Plus-Noise Cross Power Spectral Density Matrix for Own Voice Retrieval

Poul Hoang, Zheng-Hua Tan, Thomas Lunner, Jan Mark de Haan, Jesper Jensen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

In headset and hearing aid applications, it is of interest to retrieve the user's own voice in a noisy environment, e.g. for telephony applications. To do so, the cross-power spectral density (CPSD) of the interference-plus-noise is required. In this paper, a novel maximum likelihood (ML) estimator of the interference-plus-noise CPSD matrix is proposed. The proposed method is able to estimate the interference-plus-noise CPSD matrix, even during signal regions with own voice activity. The method uses a novel procedure for estimating the interference-plus-noise CPSD matrix by first estimating the interference PSD and afterwards the noise PSD in a maximum likelihood sense. Simulation experiments, where the proposed method is compared to other noise CPSD matrix estimators, show that it performs on par or better than competing methods, particularly, in situation where the interferenceto-noise ratio is large.

Original languageEnglish
Title of host publicationICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages5
PublisherIEEE
Publication dateMay 2020
Pages6939-6943
Article number9053988
ISBN (Print)978-1-5090-6632-2
ISBN (Electronic)978-1-5090-6631-5
DOIs
Publication statusPublished - May 2020
EventICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Barcelona, Spain
Duration: 4 May 20208 May 2020

Conference

ConferenceICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
CountrySpain
CityBarcelona
Period04/05/202008/05/2020
SeriesInternational Conference on Acoustics Speech and Signal Processing (ICASSP)
ISSN1520-6149

Keywords

  • Own voice retrieval
  • multi-microphone speech enhancement
  • power spectral density estimation

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