1 / 42

2004 COMP.DSP CONFERENCE

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

josiah-ward
Download Presentation

2004 COMP.DSP CONFERENCE

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. 2004 COMP.DSP CONFERENCE Survey of Noise Reduction Techniques Maurice Givens

  2. 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

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

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

  5. ANC CONFIGURATION + S Reference Noise - Input With Noise Adaptive Filter

  6. 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

  7. ANC IMPLEMENTATION Reference Noise Seismometer Signal Seismometers

  8. ALE CONFIGURATION + Reference Noise S - t Adaptive Filter

  9. 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

  10. ALE CONFIGURATION • Sample of Noisy Signal

  11. ALE CONFIGURATION • Recovered Signal Using ALE

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

  13. 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

  14. FAST FOURIER TRANSFORM • Noisy Signal

  15. FAST FOURIER TRANSFORM • Fast Fourier Transform Of Noisy Signal

  16. FAST FOURIER TRANSFORM • Fast Fourier Transform After Coefficient Shrinkage

  17. FAST FOURIER TRANSFORM • Recovered Signal Using Coefficient Shrinkage

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

  19. 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

  20. 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

  21. COEFFICIENT SHRINKAGE • Original Recorded Signal

  22. COEFFICIENT SHRINKAGE • Recovered Signal With FFT Decomposition Alone

  23. COEFFICIENT SHRINKAGE • Pulse Is Good Signal For DWT Decomposition

  24. 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

  25. 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)

  26. 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 S

  27. SIGNAL DECOMPOSITION • Alternate Filter Bank Method S

  28. 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

  29. 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

  30. 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

  31. Noise Cancellation • Block Diagram TO OTHER BANDS SIGNAL POWER COMPUTE GAIN FROM OTHER BANDS NOISE POWER Gk(n) X(n) S Y(n) FSF VAD FROM OTHER BANDS TO OTHER BANDS

  32. FREQUENCY SAMPLING FILTER • FSF Comprises Two Basis Blocks • Comb Filter • Resonator FSF Comb Filter Resonator C(z) Rk(z)

  33. COMB FILTER • Block Diagram S x(n) Z-N rN u(n) - • Comb Filter Not Necessary For Implementation

  34. Resonator

  35. RESONATOR • Block Diagram u(n) Z-1 - r cos(qk) S S y(n) - r2 Z-1 2 Z-1

  36. GOOGLE RESONATOR SEARCH

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

  38. DECISION LOGIC • Speech Present Based On Inequality • Gain Based On Inequality

  39. GAIN MODIFICATION • Gain Factor Requires Post-Emphasis

  40. 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

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

  42. QUESTIONS?

More Related