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

2. 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  Tempo can be so fast that the viewers cannot understand

3. Problem Statement  Wim De Vilder : an example

4. Problem Statement  Examples : Different pitch = Time-Scaling Original Signal Fast Version Slow Version Pitch Corrected

5. Problem Statement  The Wim De Vilder-filter and Time-Scaling ?  Automatically (in real-time) know the difference between speech and breath  Allow speech to pass  Slow down signal without distorting pitch  Digital signal processing = key to the problem  Characteristics (features) extracted from the acoustics  Difference between speech and breath (classification)  Pitch extraction from audio signal

6. Problem Statement  The Wim De Vilder-filter and Time-Scaling ?  Automatically (in real-time) know the difference between speech and breath  Allow speech to pass  Slow down signal without distorting pitch  Digital signal processing = key to the problem  Characteristics (features) extracted from the acoustics  Difference between speech and breath (classification)  Pitch extraction from audio signal

7. Problem Statement  The Wim De Vilder-filter and Time-Scaling ?  Automatically (in real-time) know the difference between speech and breath  Allow speech to pass  Slow down signal without distorting pitch  Digital signal processing = key to the problem  Characteristics (features) extracted from the acoustics  Difference between speech and breath (classification)  Pitch extraction from audio signal

8. Problem Statement  The Wim De Vilder-filter and Time-Scaling ?  Automatically (in real-time) know the difference between speech and breath  Allow speech to pass  Slow down signal without distorting pitch  Digital signal processing = key to the problem  Characteristics (features) extracted from the acoustics  Difference between speech and breath (classification)  Pitch extraction from audio signal

9. Planning Team 1 (Wim De Vilder Filter)Team 2 (Time Stretching) 30/9Problem Statement : First Group Meetings Voice activity detection : features 07/10Voice activity detection : classificatieSample rate change / framing Feature : Zero-crossing rate/periodiciteitTime Stretching : Overlap Add Synthesis (OLA) 14/10Feature : spectrale energieTime Stretching : OLA Feature : spectrale energieTime Stretching : Synchronous Overlap Add Synthesis (SOLA) 21/10Schrijven tussentijds verslag SOLA : Time Domain Auto Correlation Feature : LPCPitch Synchronous Overlap Add Synthesis (PSOLA) 25/10Deadline mid-term report 28/10Feature : cepstrale energiePitch Detection : Zero Rate Crossing 4/11Feauture : tijdsinformatiePitch Detection : Modified Zero Rate Crossing Features : combinatiePitch Detection : Auto-Correlation Techniques 12/11Bayesiaanse classificatie + Gaussian Mixture ModelsPSOLA : Implementation Bayesiaanse classificatie + Gaussian Mixture ModelsPSOLA : Implementation 18/11Real-time implementatie in Simulink 25/11Real-time implementatie in Simulink 27/11Deadline infobrochure 02/12Real-time implementatie in Simulink, preparation for demo 9/12preparation for report, presentation 16/12Presentation

10. Praktisch  2 sessies per week (seeTijdstabel P&O3)  Monday 13.50-18.00  Thursday 13.50-18.00  2 hours interaction per week  E-mail for questions/problems!  joseph.szurley@esat.kuleuven.be  bruno.defraene@esat.kuleuven.bebruno.defraene@esat.kuleuven.be