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© 2007 Texas Instruments Inc,
Content developed in partnership with Tel-Aviv University
From MATLAB® and Simulink® to Real Time with TI DSPs
Acoustic Noise Cancellation
Slide Slide 22© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Objectives
• To develop a Simulink® model of a noise reduction system using the Least Mean Squares (LMS) Algorithm.
• To run the model on the Texas Instruments C6713 DSK.
Slide Slide 33© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
What is Noise?
• In the audio sense, “noise” refers to any other signals besides those we want to listen to.
• Click on the icon to hear noise:
Slide Slide 44© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Adaptive Filter Block Diagram
Adaptive Filter Block Diagram
d(n) Desired
y(n)
e(n)
+
-x(n)
Filter InputAdaptive Filter
e(n) Error Output
Filter Output
)()( e(n) nynd
Slide Slide 55© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Adaptive Filter Equation
• The Adaptive Filter is a Finite Impulse Response Filter (FIR), with N variable coefficients w.
N
kk knxnw y(n)
0
)()(
Slide Slide 66© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
The LMS Equation
• The Least Mean Squares Algorithm (LMS) updates each coefficient on a sample-by-sample basis based on the error e(n).
• This equation minimises the power in the error e(n).
)()()( nxnenw 1)(nw kkk
Slide Slide 77© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
The Least Mean Squares Algorithm
• The value of µ (mu) is critical.
• If µ is too small, the filter reacts slowly.
• If µ is too large, the filter resolution is poor.
• The selected value of µ is a compromise.
Slide Slide 88© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Audio Noise Reduction
• A popular application of acoustic noise reduction is for headsets for pilots. This uses two microphones.
Block Diagram of a Noise Reduction Headset
d(n) = speech + noise
y(n)
e(n)
+
-
x(n) = noise'
Adaptive Filter
e(n)Speech Output
Filter Output (noise)
Far Microphone
Near Microphone
Slide Slide 99© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Noise Reduction using one Microphone
• This scheme is suitable for the C6713 DSK.
Adaptive Noise Reduction us ing a Single Input
d(n) = s + n
y(n)
e(n)
+
-
x(n) = s'+n'Adaptive Filter
e(n)Noise Output
n
Filter Outputs
Delay
Slide Slide 1010© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Operation of C6713 Noise Reduction
• Assumption: that speech does not change quickly. In this case, s = s’ + phase shift.
• If the delay is longer than the size of the filter, then noise n ≠ n’.
• The Adaptive Filter shapes s’ to make it as similar as possible as it can to s.
• Random noise cannot be shaped.
Slide Slide 1111© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Simulation
Slide Slide 1212© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
The Simulink Model
• Open the following Simulink model: “AcousticNoiseCancellation”.
Slide Slide 1313© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Setting the Step size (mu)
• The rate of convergence of the LMS Algorithm is controlled by the “Step size (mu)”.
• This is the critical variable.
Slide Slide 1414© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Trace of Input to Model
• “Input” = Signal + Noise.
Slide Slide 1515© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Trace of LMS Filter Output
• “Output” starts at zero and grows.
Slide Slide 1616© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Trace of LMS Filter Error
• “Error” contains the noise.
Slide Slide 1717© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Introduction to Laboratory
Slide Slide 1818© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Typical C6713 DSK Setup
USB to PC to +5V
Headphones Microphone
Slide Slide 1919© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Modified Model for C6713 DSK • You will build the model
“AcousticNoiseReductionDSKC6713”.
Slide Slide 2020© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Using Frames
• This model uses frames of data rather than individual bytes.
• The “Samples per frame” is set to 64.
Slide Slide 2121© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Built Model Showing Frames
• When the model is built, the frames are shown as double lines.
Slide Slide 2222© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Setting up the C6713 DSK
• Plug an microphone and computer loudspeakers / headphones into the C6713 DSK.
• Put the microphone next to a source of random noise e.g. an off-station radio.
• Speak into the microphone.
• Listen to the output.
Slide Slide 2323© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Second Model for C6713 DSK • Open the model
“C6713_LMS_Noise_Reduction_Dual_Output”.
Slide Slide 2424© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
The Second Simulink Model
• Uses a stereo signal:
– LMS Filter Output on one channel.
– LMS Filter Error on the other channel.
• Note that in order to process stereo data, the matrix “selector” and “matrix concatenation” must be used.
Slide Slide 2525© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
Things You Can Try
• Change the number of filter elements from 32 to see how many elements you need.
• Change the step-size (mu) to see how the LMS converges.
• Try other adaptive filter algorithms e.g. RLS.
Slide Slide 2626© © 2007 Texas Instruments Inc, 2007 Texas Instruments Inc,
References
• Digital Signal Processing, A Practical Approach by Emmanuel C. Ifeachor and Barrie W. Jervis. ISBN 0201-59619-9.
• Digital Signal Processing with C and the TMS320C30 by Rulph Chassaing. ISBN 0-471-55780-3.