Acoustic Noise Cancellation

2007 Texas Instruments Inc, 1

Acoustic Noise Cancellation

1 Introduction The Least Mean Squares (LMS) Algorithm can be used in a range of Digital Signal

Processing applications such as echo cancellation and acoustic noise reduction.

This laboratory shows how to design a model of LMS Noise Cancellation using

Simulink and run it on a Texas Instruments C6000 DSP.

1.1 Objectives

Design a model of LMS Noise Reduction for the Texas Instruments C6000

family of DSP devices using MATLAB and Simulink.

Modify an existing Simulink demonstration model for use as a template.

Run the project on the Texas Instruments DSK6713 with a microphone

and computer loudspeakers / headphones.

1.2 Level

Intermediate - Assumes prior knowledge of MATLAB and Simulink. It also requires

a theoretical understanding of matrices and the LMS algorithm.

1.3 Hardware and Software Requirements

This laboratory was originally developed using the following hardware and

software:

MATLAB R2006b with Embedded Target for TI C6000 and the Signal

Processing Toolbox.

Code Composer Studio (CCS) v3.1

Texas Instruments DSK6713 hardware.

Microphone and computer loudspeakers / headphones.


2 Simulation You will now start with a simple Simulink model and run it to see how it works.

2.1 Opening the Acoustic Noise Cancellation Model

Open the AcousticNoiseCancellation.mdl

Figure 1 Opening the AcousticNoiseCancellation Model

Run the model.

2.2 Inputs and Outputs of LMS Filter

The output from the LMS Filter starts at zero and grows slowly. Initially, some of

the sine wave information is lost as LMS Error.

Figure 2 LMS Filter Inputs and Outputs


2.3 LMS Filter Weights (Coefficients)

The LMS Filter Weights all start at zero and take several iterations to reach their

final values.

Figure 3 LMS Filter Weights (Coefficients)

2.4 Tuning the Model

The critical variable in the LMS Filter is the Step size (mu). This sets the rate of

convergence of the LMS filter.

Figure 4 Changing the Step size (mu) to 0.1

Double-click on the LMS Filter block and change the Step size (mu) to 0.1

Run the model.


2.5 Filter Outputs for Step size (mu) = 0.1

When the Step size (mu) is increased, LMS algorithm converges more quickly,

but at the expense of granularity the LMS Filter Output is not as smooth.

Figure 5 Input and LMS Filter Outputs for Step size (mu) = 0.1

2.6 Filter Weights for Step size (mu) = 0.1

Note that the filter weights (coefficients) do not attain smooth values, as would

be the case for smaller values of Step size (mu).

Figure 6 LMS Filter Weights for Step size (mu) = 0.1

2.7 Changing the Delay

Part of the Acoustic Noise Algorithm is the delay. The delay should ideally be at

least half a wavelength so the two inputs to the LMS Filter have different random

noise.


Figure 7 Changing the Delay

Experiment with different values of delay to see how it effects the operation of

the LMS Filter.

2.8 Changing the Number of Weights

Double-click on the LMS Block and change the Filter Size (number of Weights).

If the number of Weights is large, the algorithm will be slow to run.

If the number of Weights is too small, the filter will not remove the noise properly.

Figure 8 Changing the Filter Length

2.9 Summary

From practical experience, you should now know how to use LMS algorithm and

how you can adjust the Step size (mu), the filter delay and the number of weights

to obtain optimum performance.

You will now apply this to building a real-time model.


3 Real-Time Model You have now run the simulation and understand the operation of the LMS Filter.

You will now implement the Real-Time Acoustic Noise Cancellation Model using

the Texas Instrument C6713.

3.1 Texas Instruments DSK6713 Setup

Figure 9 Texas Instruments DSK6713 Setup

Alternatively, you can use computer loudspeakers.

3.2 Starting up Code Composer Studio

3.2.1 Connecting the DSK6713

Start Code Composer Studio for DSK6713 and use Debug -> Connect

Figure 10 Startup Screen for Code Composer Studio (CCS)


3.2.2 Opening an Existing Model

Start MATLAB 7.3.0 R2006b:

Figure 11 Opening an Existing Demo

Click on Demos. The following screen will appear:

Figure 12 Selecting the Audio Demo Models

Highlight Embedded Target for TI C6000 DSP then Audio. Click on Wavelet

Denoising. We are going to use this as our template.


3.2.3 Viewing the Original Model

The Wavelet Denoising model is now displayed.

Figure 13 - Wavelet Denoising Parent

3.2.4 Saving the Model

For convenience, save the model to the MATLAB Work directory, where most

models are stored.

Figure 14 Saving the Model to the MATLAB Work directory

3.2.5 Changing the Title

Delete the Info box. Change the title to LMS Noise Reduction. You may also

wish to move the DSK6713 icon to the left hand side.


Figure 15 - LMS Noise Reduction Parent

3.2.6 The Original Wavelet Noise Reduction

Algorithm

Double-click on the function() box. The Wavelet Noise Reduction Algorithm

model is now displayed.

Figure 16 - Wavelet Denoising Algorithm

3.2.7 Delete Blocks

Delete the blocks and connect the input directly to the output. Add a title.


Figure 17 - LMS Denoising Algorithm Template

3.2.8 Overview of the LMS Model

We are going to implement the model shown below.

We will now update the empty model by dragging-and-dropping some library

components onto the model.

Figure 18 Overview of the LMS Algorithm

3.2.9 Changing the Input to Microphone

Double-click on the blue box to the left marked DSK6713 ADC. The following

screen will appear.

Figure 19 Setting up the ADC for Mono Microphone Input


Change the ADC source to Mic In.

If you have a quiet microphone, select +20dB Mic gain boost.

Set the Sampling rate (Hz) to 48 kHz.

Set the Samples per frame to 64.

When done, click on OK.

Important: Make sure the Stereo box is empty.

3.2.10 The DAC Settings

The DAC settings need to match those of the ADC. Check that it uses the same

sampling rates. Click on OK.

Figure 20 Setting the DAC Parameters

3.2.11 Adding an LMS Block

The Simulink block for LMS is to be found in the Signal Processing Toolbox.

Select View -> Library Browser -> Signal Processing Blockset ->Filtering->

Adaptive Filters.

Highlight Adaptive Filters. Drag-and-drop the LMS Filter block onto the model.

Figure 21 Adding an LMS Filter Block


3.2.12 Setting the LMS Filter Parameters

The most critical variable in an LMS filter is the Step size (mu).

If mu is too small, the filter has very fine resolution, but reacts too slowly to the

audio signal.

If mu is too great, the filter reacts very quickly, but the error also remains large.

We will start with 0.005.

Figure 22 Setting the Parameter Set size (mu)

3.2.13 Adding a Delay

From the Signal Processing Blockset, highlight Signal Operations. Drag-and-

drop the Delay1 block onto the model.

Figure 23 Adding a Delay

1 Since we are working with frames, the delay from Discrete Components library

will not work!


3.2.14 Setting the Delay Parameters

Because we are working with frames of 64 samples, it is convenient configure the

delay using frames. Double-click on the Delay block.

Change the Delay units to Frames.

Set the Delay (frames) to 1. This makes the delay 64 samples.

Figure 24 Setting the Delay Size

3.2.15 Adding a DIP Switch and LED

So we can hear the difference without LMS denoising and with LMS noise

reduction, we will use a DIP switch of the DSK6713.

Figure 25 Adding a Switch and LED

Select View -> Library Browser -> Embedded Target for TI C6000 DSP. Highlight

DSK6713 Board Support.

Drag-and-drop the Switch block onto the model. Also drag-and-drop the LED

block onto the model.


3.2.16 DIP Switch Settings

The DIP switch needs to be configured. Double-click on the Switch block.

Select all the boxes and set Data type to Integer. The Sample time should

also be set to 1.

Figure 26 Setting up the DIP Switch Values

3.2.17 Adding a Constant, Switch and Relational

Operator

We now need to setup a way to switch between straight through without noise

reduction and with LMS noise reduction.

Select View -> Library Browser -> Simulink. Highlight Commonly Used Blocks.

Drag-and-drop a Constant onto the model.

Drag-and-drop a Switch block onto the model.

Drag-and-drop a Relational Operator block onto the model.

Figure 27 Selecting the Commonly Used Blocks


3.2.18 Setting the Constant Value

The switch values lie between 0 and 15. We will use switch values 0 and 1.

Double-click on the Constant block. Set the Constant value to 1 and the

Sample time to inf.

Figure 28 Setting the Echo Delay Gain

3.2.19 Setting the Constant Data Type

Click on the Signal Data Types tab. Set the Output data type mode to int16.

This is compatible with the DAC on the DSK6713.

Figure 29 Data Type Conversion to 16-bit Integer


3.2.20 Setting the Relational Operator Type

Double click on the Relational Operator block. Change the Relational operator

to ==. Click on the Signal Data Types tab.

Figure 30 Changing the Relational Operator

3.2.21 Setting the Relational Operator Data Type

Set the Output data type mode to Boolean. Click on OK.

Figure 31 Changing the Output Data Type


3.2.22 Joining the Blocks

Move the blocks and join them as shown in the Figure below.

Figure 32 Joining the Blocks

3.2.23 Returning to the Parent System

From the Toolbar, select the Up Arrow icon. This returns you to the next higher

level.

Figure 33 Returning to the Parent System

3.3 Building the Model

3.3.1 Selecting Real-Time Workshop

Select Tools -> Real-Time Workshop -> Build Model.

Figure 34 Building the Model


3.3.2 Frames Displayed on Model

When built, the single lines are replaced by double lines. This shows frames.

Figure 35 Frames

3.3.3 The Completed Model Running on Code

Composer Studio

From the folders on the left, select the source code for the project.

Figure 36 The Completed Model Running in Code Composer Studio


3.4 Different Settings on the DSK6713

3.4.1 Microphone Straight Through to Loudspeakers

To check out the microphone and loudspeakers, set the DIP switches on the

DSK6713 as follows:

Figure 37 Switch Position 0

The microphone is fed directly to the loudspeakers. There is no LMS noise

reduction.

3.4.2 Switch Position for LMS Noise Reduction

To run the LMS Noise Reduction subsystem, set the DIP switch to 1.

Figure 38 Switch Position 1 for LMS Noise Reduction


4 Some Things to Try You may wish to experiment with different settings. Here are some suggestions.

4.1 Experiment with LMS Filter Settings

Change the value of Step size (mu) between 0.0001 and 0.5. This is the critical

value.

Low values of mu give good resolution, but a slow reaction time.

High values of mu give less resolution, but faster reaction times.

Find the best value of mu for noise reduction on the TI DSK6713.

Figure 39 - Configuring the LMS Filter Block Parameters

4.2 Experiment with LMS Filter Settings

Try different value of Filter Length. What is the minimum value that will allow

the filter to work correctly?

4.3 Change from LMS Filter to RLS Filter

Inside the Adaptive Filters are different LMS types. Which are suitable for LMS

denoising and which are not?


Figure 40 Available Adaptive Filter Types

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Acoustic Noise Cancellation

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