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Wim De Vilder – filter : verwijderen van ademhalingsruis uit spraaksignalen

Probleemoplossen en ontwerpen, deel 3. Wim De Vilder – filter : verwijderen van ademhalingsruis uit spraaksignalen. Problem Statement. Newscasters present the news with a very quick tempo Between two sentences they require a large breath Can be a distraction for the viewers

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Wim De Vilder – filter : verwijderen van ademhalingsruis uit spraaksignalen

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  1. Probleemoplossen en ontwerpen, deel 3 Wim De Vilder – filter : verwijderen van ademhalingsruis uit spraaksignalen

  2. Problem Statement • Newscasters present the newswith a veryquick tempo • Betweentwosentencestheyrequire a largebreath • Canbe a distractionfor the viewers • Tempo canbesofastthat the viewers cannotunderstand

  3. Problem Statement • Wim De Vilder : anexample

  4. Problem Statement • Examples : Different pitch = Time-Scaling • Original Signal • FastVersion • Slow Version • PitchCorrected

  5. Problem Statement • The Wim De Vilder-filter and Time-Scaling ? • Automatically (in real-time) know the differencebetween speech and breath • Allow speech to pass • Slow down signal without distortingpitch • Digital signal processing = key to the problem • Characteristics (features) extractedfrom the acoustics • Differencebetween speech and breath (classification) • Pitchextractionfrom audio signal

  6. Problem Statement • The Wim De Vilder-filter and Time-Scaling ? • Automatically (in real-time) know the differencebetween speech and breath • Allow speech to pass • Slow down signal without distortingpitch • Digital signal processing = key to the problem • Characteristics (features) extractedfrom the acoustics • Differencebetween speech and breath (classification) • Pitchextractionfrom audio signal

  7. Problem Statement • The Wim De Vilder-filter and Time-Scaling ? • Automatically (in real-time) know the differencebetween speech and breath • Allow speech to pass • Slow down signal without distortingpitch • Digital signal processing = key to the problem • Characteristics (features) extractedfrom the acoustics • Differencebetween speech and breath (classification) • Pitchextractionfrom audio signal

  8. Problem Statement • The Wim De Vilder-filter and Time-Scaling ? • Automatically (in real-time) know the differencebetween speech and breath • Allow speech to pass • Slow down signal without distortingpitch • Digital signal processing = key to the problem • Characteristics (features) extractedfrom the acoustics • Differencebetween speech and breath (classification) • Pitchextractionfrom audio signal

  9. Planning

  10. Praktisch • 2 sessies per week (seeTijdstabel P&O3) • Monday 13.50-18.00 • Thursday 13.50-18.00 • 2 hoursinteraction per week • E-mail forquestions/problems! • joseph.szurley@esat.kuleuven.be • bruno.defraene@esat.kuleuven.be

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