---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Aleksej
      foaf_name: Chinaev, Aleksej
      foaf_surname: Chinaev
  - foaf_Person:
      foaf_givenName: Jahn
      foaf_name: Heymann, Jahn
      foaf_surname: Heymann
      foaf_workInfoHomepage: http://www.librecat.org/personId=9168
  - foaf_Person:
      foaf_givenName: Lukas
      foaf_name: Drude, Lukas
      foaf_surname: Drude
      foaf_workInfoHomepage: http://www.librecat.org/personId=11213
  - foaf_Person:
      foaf_givenName: Reinhold
      foaf_name: Haeb-Umbach, Reinhold
      foaf_surname: Haeb-Umbach
      foaf_workInfoHomepage: http://www.librecat.org/personId=242
  dct_date: 2016^xs_gYear
  dct_language: eng
  dct_title: Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs@
...