Detail publikace

Nonlinear adaptive models for speech enhancement algorithms

KOULA, I., ZEZULA, R.

Originální název

Nonlinear adaptive models for speech enhancement algorithms

Typ

článek v časopise - ostatní, Jost

Jazyk

angličtina

Originální abstrakt

This paper is focused on comparison of the effectiveness of three different one channel speech enhancement algorithms. These speech enhancement algorithms are based on spectral subtraction method and on different approaches to noise spectrum estimation. The first algorithm estimates noise spectrum on the basis of its statistical characters. Next two algorithms estimate noise spectrum by nonlinear adaptive models. These models are arti-ficial neural networks and adaptive neural-fuzzy interface system. An algo-rithm giving efficiency enhancement of the obtained results was based on the hit rate recognition from the output of a phoneme based speech recognizer based on hidden Markov's models and implemented through the HTK toolkit.

Klíčová slova

estimate of noise power spectral density, one-channel noise cancelation algorithm, spectral subtraction, artificial neural network, adaptive neural-fuzzy interface system

Autoři

KOULA, I., ZEZULA, R.

Vydáno

30. 9. 2007

Nakladatel

GESTS

Místo

Korea

ISSN

1738-9682

Periodikum

International Transactions on Communication and Signal Processing

Ročník

10

Číslo

7

Stát

Korejská republika

Strany od

138

Strany do

145

Strany počet

9

BibTex

@article{BUT44688,
  author="Ivan {Koula} and Radek {Zezula}",
  title="Nonlinear adaptive models for speech enhancement algorithms",
  journal="International Transactions on Communication and Signal Processing",
  year="2007",
  volume="10",
  number="7",
  pages="138--145",
  issn="1738-9682"
}