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<titleInfo><title>Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs</title></titleInfo>





<name type="personal">
  <namePart type="given">Aleksej</namePart>
  <namePart type="family">Chinaev</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Jahn</namePart>
  <namePart type="family">Heymann</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">9168</identifier></name>
<name type="personal">
  <namePart type="given">Lukas</namePart>
  <namePart type="family">Drude</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">11213</identifier></name>
<name type="personal">
  <namePart type="given">Reinhold</namePart>
  <namePart type="family">Haeb-Umbach</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">242</identifier></name>







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<abstract lang="eng">A noise power spectral density (PSD) estimation is an indispensable component of speech spectral enhancement systems. In this paper we present a noise PSD tracking algorithm, which employs a noise presence probability estimate delivered by a deep neural network (DNN). The algorithm provides a causal noise PSD estimate and can thus be used in speech enhancement systems for communication purposes. An extensive performance comparison has been carried out with ten causal state-of-the-art noise tracking algorithms taken from the literature and categorized acc. to applied techniques. The experiments showed that the proposed DNN-based noise PSD tracker outperforms all competing methods with respect to all tested performance measures, which include the noise tracking performance and the performance of a speech enhancement system employing the noise tracking component.</abstract>

<originInfo><dateIssued encoding="w3cdtf">2016</dateIssued>
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<language><languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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<relatedItem type="host"><titleInfo><title>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</title></titleInfo>
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     <url>https://groups.uni-paderborn.de/nt/pubs/2016/ChHeyDrHa16_Presentation.pdf</url>
  
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<bibtex>@inproceedings{Chinaev_Heymann_Drude_Haeb-Umbach_2016, title={Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs}, booktitle={12. ITG Fachtagung Sprachkommunikation (ITG 2016)}, author={Chinaev, Aleksej and Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold}, year={2016} }</bibtex>
<mla>Chinaev, Aleksej, et al. “Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs.” &lt;i&gt;12. ITG Fachtagung Sprachkommunikation (ITG 2016)&lt;/i&gt;, 2016.</mla>
<short>A. Chinaev, J. Heymann, L. Drude, R. Haeb-Umbach, in: 12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016.</short>
<apa>Chinaev, A., Heymann, J., Drude, L., &amp;#38; Haeb-Umbach, R. (2016). Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs. In &lt;i&gt;12. ITG Fachtagung Sprachkommunikation (ITG 2016)&lt;/i&gt;.</apa>
<chicago>Chinaev, Aleksej, Jahn Heymann, Lukas Drude, and Reinhold Haeb-Umbach. “Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs.” In &lt;i&gt;12. ITG Fachtagung Sprachkommunikation (ITG 2016)&lt;/i&gt;, 2016.</chicago>
<ieee>A. Chinaev, J. Heymann, L. Drude, and R. Haeb-Umbach, “Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs,” in &lt;i&gt;12. ITG Fachtagung Sprachkommunikation (ITG 2016)&lt;/i&gt;, 2016.</ieee>
<ama>Chinaev A, Heymann J, Drude L, Haeb-Umbach R. Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs. In: &lt;i&gt;12. ITG Fachtagung Sprachkommunikation (ITG 2016)&lt;/i&gt;. ; 2016.</ama>
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