Wim De Vilder – filter : verwijderen van ademhalingsruis uit

spraaksignalen

Probleemoplossen en ontwerpen, deel 3

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

Problem Statement

Wim De Vilder : an example

Problem Statement

Examples : Different pitch = Time-Scaling

Original Signal

Fast Version

Slow Version

Pitch Corrected

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

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

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

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

PlanningTeam 1 (Wim De Vilder Filter) Team 2 (Time Stretching)

30/9 Problem Statement : First Group Meetings

Voice activity detection : features Voice activity detection : features

07/10 Voice activity detection : classificatie Sample rate change / framing

Feature : Zero-crossing rate/periodiciteit Time Stretching : Overlap Add Synthesis (OLA)

14/10 Feature : spectrale energie Time Stretching : OLA

Feature : spectrale energieTime Stretching : Synchronous Overlap Add Synthesis (SOLA)

21/10 Schrijven tussentijds verslag SOLA : Time Domain Auto Correlation

Feature : LPC Pitch Synchronous Overlap Add Synthesis (PSOLA)

25/10 Deadline mid-term report

28/10 Feature : cepstrale energie Pitch Detection : Zero Rate Crossing

4/11 Feauture : tijdsinformatie Pitch Detection : Modified Zero Rate Crossing

Features : combinatie Pitch Detection : Auto-Correlation Techniques

12/11Bayesiaanse classificatie + Gaussian Mixture Models PSOLA : ImplementationBayesiaanse classificatie + Gaussian Mixture Models PSOLA : Implementation

18/11 Real-time implementatie in Simulink

Real-time implementatie in Simulink

25/11 Real-time implementatie in Simulink

Real-time implementatie in Simulink

27/11 Deadline infobrochure

02/12 Real-time implementatie in Simulink, preparation for demo

Real-time implementatie in Simulink, preparation for demo

9/12 preparation for report, presentation

16/12 Presentation

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.be