1. Communications & Multimedia Signal Processing 1 Speech Communication for Mobile and Hands-Free Devices in Noisy Environments EPSRC Project GR/S30238/01 Brunel, Southampton, UEA, Queens, TTPCOM Project meeting 28 Jan 04

2. Communications & Multimedia Signal Processing 2 De-noising Speech Qin Yan, Vaseghi Analysis-synthesis restoration with performance indicators Speech Recognition in Noise Esi Zavaeri, Vaseghi Focus on robust features and minimum discrimination error in noise Project Sections Speaker Adaptation and Recognition in Noise Focus on Speaker features and adaptation in noise Jon Darch, Milner Modelling Non-stationary Multi-source Noise Environments Ioannis, Paul White Analysis, Modelling, Classification, Prediction of noise and Environment assessment

3. Communications & Multimedia Signal Processing 3 De-noising Speech: Main Features Noise Spectral-Time Tracker (Ioannis with Qin Yan) Estimates of noise tracks during speech inactive/active periods Noise tracks are dominant noise frequency channels Noisy Speech spectral-time Tracker Estimates of speech tracks in noise Estimates of speech signal to noise ratio (SNRs) across tracks Performance Measures, Monitors and SNR Guarantees De-noising LP model poles Pitch harmonics tracking in noise VAD based on spectral distance from noise, formants and pitch track Glottal Pulse Estimation (Emir Turajlic)

4. Communications & Multimedia Signal Processing 4 VAD Noise Reduction LP-SS Noise Model Templates Noise Formant Tracking LP pole Estimation Pitch Tracking Excitation Estimation Signal Re- composition Noisy Speech Enhanced Speech Noisy Model Perceptual Model A block Diagram of Noisy Speech Processing System Version 1.0 Speech and Noise Model Decomposition Work plan for Feb-May 04 Noise modelled with LP spectral template or GMM Speech LP spectra obtained from LP spectral subtraction Utilisation of LP predictions across formant tracks `Pitch tracking and excitation denoising Restoration via Wiener filtering or LP re-composition

5. Communications & Multimedia Signal Processing 5 VAD Noise Reduction LP-SS Noise Model Templates Noise Time-averaged Adaptive Noise LP Spectra (current work) Estimated from speech inactive periods (VAD) Statistics of update periods for mobiles, hands-free (with head phones and with loudspeakers on) (Ioannis) GMM of Noise LP-Spectra resulting in a several noise templates The issue of predictability of noise tracks and finite-state noise models Noise Models LP AnalysisLP Spectrum Averaging or GMM [a][a] N LP (f) Noise Models Noise

6. Communications & Multimedia Signal Processing 6 Spectrograms of Car Noise Spectrogram of Car Noise shows noise tracks LP Spectrogram of Car Noise

7. Communications & Multimedia Signal Processing 7 Spectrograms of Train Noise Spectrogram of train Noise LP Spectrogram of train Noise

8. Communications & Multimedia Signal Processing 8 VAD In Noise Noise Reduction LP-SS Noise Model Templates Noise LP-based Spectrum NL Spectral Subtraction LP spectrum Smoothing Input Noisy Speech Pole-Track Smoothing Noise Model Templates Speech Spectral Tracks LP Spectral Envelope LP Model Coeffs Illustration of spectral subtraction for removing noise from LP model spectrum Speech spectral tracks: Formant track and pitch track De-noising LP model spectrum

9. Communications & Multimedia Signal Processing 9 VAD Noise Reduction LP-SS Noise Model Templates Noise Formant Tracking LP pole Estimation Pitch Tracking Excitation Estimation Signal Re- composition Noisy Speech Enhanced Speech Noisy Model Perceptual Model Formant tracking in noise Predictive formant tracking to include dynamic parameters Speech state indicator of expected formant continuity LP pole estimation from formant tracks

10. Communications & Multimedia Signal Processing 10 Pitch Tracking Excitation Estimation Pitch tracking in noise Pitch tracking using adaptive band-pass filter centred on pitch estimate Pitch Harmonics tracking State-based excitation restoration Glottal pulse models

11. Communications & Multimedia Signal Processing 11 VAD In Noise Noise Reduction LP-SS Noise Model Templates Noise Formant Tracking LP pole Estimation Pitch Tracking Excitation Estimation Signal Re- composition Noisy Speech Enhanced Speech Noisy Model Perceptual Model Two alternative restoration methods: LP-based Wiener Filters Re-composition of de-noised LP model with de-noised Excitation Signal Restoration

12. Communications & Multimedia Signal Processing 12 Performance Indicators for de-noising Performance SNRs before and after de-noising Distortion measures and prediction Performance bounds and guarantees

13. Communications & Multimedia Signal Processing 13 Dissemination De-noising Website De-nosing software Database of noise with public access Invitation of industry to research meetings