About: Noise cancellation algorithms for speech signal distorted in telecommunication networks.     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • This paper aims to provide an evaluation of the effectiveness of three different speech noise power spectrum estimation algorithms The evaluation of their efficiency was based on the hit rate recognition obtained at the output of an HMM phoneme based speech recognizer. Noisy speech consisted of 100 speech sentences randomly extracted from the NTIMIT database. The best speech noise power spectrum estimator proved to be a procedure based on the arithmetic average of the power spectrums obtained from signal frames where no speech activity was detected. The noise spectrum estimate provide by either a four layer MLP neural network, or an Adaptive Neural Fuzzy Inference System (ANFIS) proved to give lower performance than the average noise spectrum estimator, even though both of them are able to detect some of the noise features and the ANFIS performance are better than those obtained from the MLP neural network.
  • This paper aims to provide an evaluation of the effectiveness of three different speech noise power spectrum estimation algorithms The evaluation of their efficiency was based on the hit rate recognition obtained at the output of an HMM phoneme based speech recognizer. Noisy speech consisted of 100 speech sentences randomly extracted from the NTIMIT database. The best speech noise power spectrum estimator proved to be a procedure based on the arithmetic average of the power spectrums obtained from signal frames where no speech activity was detected. The noise spectrum estimate provide by either a four layer MLP neural network, or an Adaptive Neural Fuzzy Inference System (ANFIS) proved to give lower performance than the average noise spectrum estimator, even though both of them are able to detect some of the noise features and the ANFIS performance are better than those obtained from the MLP neural network. (en)
  • Tento článek ukazuje možnost využití systémů umělé inteligence v algoritmech pro zvýraznění řeči v hlučném pozadí. Článek porovnává efektivitu tří odlišných systémů pro potlačení šumu založených na metodě spektrální subtrakce. První systém odhaduje spektrum šumu na základě jeho statistických vlastností. Další dva systémy odhadují spektrum šumu pomocí nelineárních adaptivních modelů. Efektivita popsaných algoritmů je vyhodnocena na základě úspěšnosti rozpoznání zpracovaných řečových nahrávek počítačovým rozpoznávačem řeči založeným na skrytých Markovových modelech. Algoritmy jsou testovány na databázi NTIMIT obsahující krátké nahrávky řečových promluv přenesené skutečnou telekomunikační sítí americké firmy NYTEX. (cs)
Title
  • Noise cancellation algorithms for speech signal distorted in telecommunication networks.
  • Noise cancellation algorithms for speech signal distorted in telecommunication networks. (en)
  • Algoritmy pro odstraňování šumu v řeči zkreslené telekomunikační sítí (cs)
skos:prefLabel
  • Noise cancellation algorithms for speech signal distorted in telecommunication networks.
  • Noise cancellation algorithms for speech signal distorted in telecommunication networks. (en)
  • Algoritmy pro odstraňování šumu v řeči zkreslené telekomunikační sítí (cs)
skos:notation
  • RIV/00216305:26220/06:PU64281!RIV07-GA0-26220___
http://linked.open.../vavai/riv/strany
  • 1-7
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/06/1233), Z(MSM0021630513)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 488756
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26220/06:PU64281
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • spectral subtraction, thresholdig, neural network, ANFIS, speech recognizer (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [0A74D77577E2]
http://linked.open...v/mistoKonaniAkce
  • Praha
http://linked.open...i/riv/mistoVydani
  • česká republika, Praha
http://linked.open...i/riv/nazevZdroje
  • 16th Czech-German Workshop on speech processing
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Koula, Ivan
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Ústav radiotechniky a elektroniky AV ČR
https://schema.org/isbn
  • 80-86269-15-9
http://localhost/t...ganizacniJednotka
  • 26220
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 77 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software