2. 2004 COMP.DSP CONFERENCE Survey of Noise Reduction Techniques Maurice Givens

3. NOISE REDUCTION TECHNIQUES Minimum Mean-Squared Error (MMSE) –Least Squares (LS) –Recursive Least Squares (RLS) –Least Mean Squares (LMS, NLMS) Coefficient Shrinkage –Fast Fourier Transform (FFT) Decomposition –Wavelet Transform Decomposition (CWT, DWT) Spectral (Sub-Band) Subtraction –Blind Adaptive Filter (BAF) –Sub-Band Decomposition Using Orthogonal Filter Banks –Wavelet Decomposition –Fast Fourier Transform (FFT) Decomposition –Frequency Sampling Filter (FSF) decomposition

4. MINIMUM MEAN-SQUARED ERROR LS, RLS, LMS Similar Operation Seek to minimize mean-squared error Will Look At LMS

5. LMS Two Types Of Noise Reduction Techniques With LMS –Adaptive Noise Cancellation (ANC) –Adaptive Line Enhancement (ALE) Similar Configurations h(n+1) = h(n) +  e(n) x(n) x(n) T x(n)

6. ANC CONFIGURATION Adaptive Filter + -  Input With Noise Reference Noise

7. ANC CONFIGURATION ANC Uses Adaptive Filter For MMSE ANC Requires Reference Noise Signal ANC Based On Bernard Widrow’s LMS Adaptive Filter ANC Can Only Recover Correlated Signals From Uncorrelated Noise Error Signal Is Recovered (Denoised) Signal

8. ANC IMPLEMENTATION Signal Seismometers Reference Noise Seismometer

9. ALE CONFIGURATION Adaptive Filter + Reference Noise  - 

10. ALE CONFIGURATION ALE Uses Adaptive Filter For MMSE ALE Does Not Require Reference Noise Signal ALE Uses Delay To Produce Reference Signal ALE Can Only Recover Correlated Signals From Uncorrelated Noise ALE Based On Bernard Widrow’s LMS Adaptive Filter Filter Output Signal Is Recovered (Denoised) Signal

11. ALE CONFIGURATION Sample of Noisy Signal

12. ALE CONFIGURATION Recovered Signal Using ALE

13. ALE IMPLEMENTATION Example of Noise and Tone on a Speech Segment Speech With Tone Cleaned Speech Speech With Noise Cleaned Speech

14. COEFFICIENT SHRINKAGE Fast Fourier Transform –Decomposition Of Signal Using Orthogonal Sine - Cosine Basis Set –White Noise Shows As Constant “Level” In Decomposition –Values Of Fourier Transform Below A Threshold Are Reduced to Zero Or Reduced By Some Value –Inverse Fourier Transform is Used To Produce Recovered Signal Wavelet Transform –Decomposition Of Signal Using A Special Orthogonal Basis Set –White Noise Shows As Small Values, Not Necessarily Constant –Wavelet Transform Values Below A Threshold Are Reduced to Zero Or Reduced By Some Value –Inverse Wavelet Transform is Used To Produce Recovered Signal –Have Both Continuous (CWT) And Discrete (DWT) Wavelets

15. FAST FOURIER TRANSFORM Noisy Signal

16. FAST FOURIER TRANSFORM Fast Fourier Transform Of Noisy Signal

17. FAST FOURIER TRANSFORM Fast Fourier Transform After Coefficient Shrinkage

18. FAST FOURIER TRANSFORM Recovered Signal Using Coefficient Shrinkage

19. WAVELET DECOMPOSITION Special Orthogonal High Pass And Low Pass Filters Down Sample By 2 Up Sample By 2

20. WAVELET TRANSFORM Important Characteristics Of Wavelet Transform –Basis Function Need Not Be Orthogonal If Perfect Reconstruction Is Not Needed –Wavelet Transform Very Good For Maintaining Edges In Signal –Wavelet Transform Excellent For Image Noise Reduction Because Images Have Sharp Edges –Wavelet Transform Not Very Good For Signals Like Speech When Noise Is High In Level –DWT Not Discrete Version Of CWT Like Fourier Transform And Discrete Fourier Transform

21. COEFFICIENT SHRINKAGE Variant Can Use Both FFT and DWT –Astro-Physics Professor At U of C Needed Noise Reduction For Cosmic Pulses Recorded. –Pulses In Middle Of Radio Spectrum –Could Not Recover With FFT Decomposition And Coefficient Shrinkage –Asked For Help

22. COEFFICIENT SHRINKAGE Original Recorded Signal

23. COEFFICIENT SHRINKAGE Recovered Signal With FFT Decomposition Alone

24. COEFFICIENT SHRINKAGE Pulse Is Good Signal For DWT Decomposition

25. SPECTRAL SUBTRACTION Fast Fourier Decomposition Sub-Band Decomposition Using Filter Banks Wavelet Decomposition (Sub-Band Decomposition Using Orthogonal Filter Banks) Blind Adaptive Filter (BAF) Frequency Sampling Filter Decomposition

26. GENERAL SCHEME Spectral Subtraction Uses Same General Scheme –Decompose Signal Into Spectrum –Determine Signal-To-Noise Ratio For Each Decomposition Bin –Vary Level Of Each Decomposition Bin Based On SNR –Convert Decomposed Signal Back Into Recovered Signal (Inverse Decomposition)

27. SIGNAL DECOMPOSITION METHODS FFT –Decomposes Signal Into Frequency Bins –SNR Of Each Bin Is Determined –Inverse FFT To Recover Denoised Signal Filter Bank (QMF) –Bandpass Filters Decompose Signal Into Frequency Bands –SNR Of Each Band Is Determined –Inverse Filter And Superposition To Recover Denoised Signal 

28. SIGNAL DECOMPOSITION Alternate Filter Bank Method 

29. SIGNAL DECOMPOSITION METHODS Wavelet –Similar To Filter Bank –Can Be Low Pass And High Pass Filters Only –Can Be Bandpass Filters Called Modulated Cosine Filters –SNR Of Each Band Is Determined –Inverse Filter And Superposition To Recover Denoised Signal –Can Be Complete Wavelet Packet Tree

30. BLIND ADAPTIVE FLTER BAF –Two Methods –First Is Not Spectral Subtraction By Itself BAF Is Used To Determine Parameters Of Noise Spectrum Derived From Parameters FFT, QMF, Wavelet, Or FSF Decomposition Noise Spectrum Used As Basis For Level Gain –Second Used By Itself BAF Is Used To Determine Parameters Of Noise Filter Signal With Inverse Parameters To Whiten Noise Use Any Method To Reduce White Noise Use Parameters To Recover Denoised Signal

31. NOISE CANCELLATION USING FSF Similar To Filter Bank And FFT Uses FSF For Decomposition Calculates SNR For Each Frequency Band Adjusts Level Of Each Frequency Band Based On SNR Recovers Denoised Signal Through Superposition

32. Noise Cancellation Block Diagram X(n) FSF VAD  G k (n) Y(n) SIGNAL POWER FROM OTHER BANDS NOISE POWER COMPUTE GAIN TO OTHER BANDS

33. FREQUENCY SAMPLING FILTER FSF Comprises Two Basis Blocks –Comb Filter –Resonator FSF Comb FilterResonator C(z)R k (z)

34. x(n) Comb Filter Not Necessary For Implementation Z -N rNrN  u(n) COMB FILTER Block Diagram -

35. Resonator

36. RESONATOR Block Diagram u(n) r2r2 2 Z -1 r cos(  k )  Z -1 - - y(n)

37. GOOGLE RESONATOR SEARCH

38. VOICE ACTIVITY DETECTOR Calculate Power In A Formant (Usually First)

39. DECISION LOGIC Speech Present Based On Inequality Gain Based On Inequality

40. GAIN MODIFICATION Gain Factor Requires Post-Emphasis

41. OTHER CONSIDERATIONS Output Level Is Lower After Noise Reduction –Solution: Increase Signal By Scaling Add A Portion Of Original Signal To Noise-Reduced Output –Can Help Mitigate Tinny Sound –Helpful If Lower Level Signals Are Overly Suppressed Perform Algorithm Fewer Times When Speech Is Absent Perform Algorithm On Sub-Set Of Frequency Bins Each Sampling Period Can Add Non-Linear Center Clipper To Algorithm

42. EXAMPLE Recording From Live Cellular Traffic Original Noisy Sample After Noise Reduction Original Noisy Sample After Noise Reduction

43. QUESTIONS?