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Transform-domain speech periodicity enhancement with adaptive coefficient weighting
KTH, School of Electrical Engineering (EES), Sound and Image Processing (Closed 130101).
2011 (English)In: 2011 International Symposium on Intelligent Signal Processing and Communications Systems: "The Decade of Intelligent and Green Signal Processing and Communications", ISPACS 2011, 2011Conference paper, Published paper (Refereed)
Abstract [en]

In our previous study of speech periodicity enhancement, the linear prediction residual signal was decomposed into periodic and aperiodic components using two-stage transforms. In the transform domain, the periodic component of the signal is concentrated and represented by a small portion of the coefficients. The respective coefficients were weighted and emphasized to enhance periodicity of the signal against noise. Fixed weights were used for different sets of the coefficients. With the fixed weights, it is observed that unvoiced and voiced-unvoiced transition signals are excessively attenuated and perceptible artificial periodicity are generated in these speech segments. In this study, we propose an adaptive weighting method. For voiced speech, the periodic component is strong and the respective transform coefficients shows a high energy level. In contrast, for unvoiced speech, periodicity is weak and the corresponding coefficients are small. The weights for the coefficients are adaptively adjusted according to the energy level of the periodic component. With the adaptive weights, the periodic component of voiced speech can be effectively emphasized and restored while the aperiodic parts in unvoiced speech are retained.

Place, publisher, year, edition, pages
2011.
Keyword [en]
coefficient weighting, noise robustness, Speech periodicity enhancement, Adaptive weighting, Adaptive weights, Aperiodic components, Artificial periodicity, Energy level, High energy, Linear prediction, Periodic components, Residual signals, Speech segments, Transform coefficients, Transform domain, Two stage, Unvoiced speech, Voiced speech, Communication systems, Speech analysis, Speech recognition, Signal processing
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-149796DOI: 10.1109/ISPACS.2011.6146126Scopus ID: 2-s2.0-84863167993ISBN: 9781457721663 (print)OAI: oai:DiVA.org:kth-149796DiVA: diva2:741541
Conference
19th IEEE International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2011; Chiang Mai, Thailand, 7-9 December, 2011
Note

QC 20140828

Available from: 2014-08-28 Created: 2014-08-27 Last updated: 2014-10-03Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf