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ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 02, March 2012



   Real-Time Active Noise Cancellation with Simulink
             and Data Acquisition Toolbox
                 Vaibhav Narula1, Mukul Sagar2, Pranab Joshi2,Puneet S. Mehta2,Sudhanshu Tripathi2
             1
               Amity School of Engineering and Technology(Electronics and Communication), New Delhi, India
                                             Email: vaibhav3589@gmail.com
             2
               Amity School of Engineering and Technology(Electronics and Communication), New Delhi, India
                Email: {mukulsagar1989, joshi.pranab04, puneetmehta90, sudhanshutripathi14}@gmail.com


Abstract— This paper presents the feasibility of implementing           used in ANC (Active Noise Cancellation) applications. The
single channel negative feedback Active Noise Cancellation              method uses a noisy signal as primary input and a reference
technique using adaptive filters in Real-time environment[1].           input that consists of noise correlated in some arbitrary manner
In order to establish the suitability and credibility of LMS            with the primary noise. By adaptively filtering and subtracting
Algorithm for adaptive filtering in real world scenario, its
                                                                        the reference input from the primary input, the output of the
efficiency was tested beyond system based ideal simulations.
Within the MATLAB® software environment two different                   adaptive filter will be the error signal, which acts as a feedback
methods were used to perform Real-time ANC namely                       to the adaptive filter. With this setup, the adaptive filter will
Simulink® and Data Acquisition ToolboxTM. Human voice is                be able to cancel the noise and obtain a less noisy signal
used as test signal. For processing and performing adaptive             estimate. Adaptive filter is generally defined as a filter whose
filtering, Block LMS Filter was utilised in Simulink and Error          characteristics can be modified to achieve some objective
Normalised Step Size algorithm was used in between input                and is usually assumed to accomplish this adaptation
and output of Signals by DAQ (Data Acquisition) toolbox                 automatically, without the need for substantial intervention
interface. A general method of using DAQ commands has been              by user. Here, an adaptive filter is needed that is capable of
employed which also allows for almost any kind of complex
                                                                        altering its impulse response as required for the noise
real-time audio processing and is quite easy to follow.
                                                                        cancellation application. Figure 1 shows the general structure
Index Terms— Active Noise Cancellation, Adaptive Filters,               of an adaptive filter. Figure 1 is composed of three blocks as
LMS, ENSSLMS, Simulink, Data Acquisition, Real-time                     shown. The first block is Adaptive filter block which shows
                                                                        what type of digital filter is used in the design of the adaptive
                         I. INTRODUCTION                                filter. The second block depicts filter adaptation rules which
                                                                        show the updating algorithm used for the adaptive filter to
    Active noise cancellation is basically the electro-acoustic         update its parameters, and last block as its name shows
generation of a sound field to cancel an unwanted existing              performs the quality assessment or simply comparison in an
sound field. The active noise cancellation system is known              adaptive system, the output of this block is the error signal
as an “adaptive” system as it can adapt itself to changing              which is fed back to filter adaptation rules block to find new
characteristics of the noise to be cancelled and changing               parameters. In Figure 1, the filter could be implemented in
environmental conditions that affect the acoustic field. An             analogue or digital, which here a digital filter is used.
Active noise cancellation is basically the electro-acoustic
generation of a sound field to cancel an unwanted existing
sound field. The active noise cancellation system is known
as an “adaptive” system as it can adapt itself to changing
characteristics of the noise to be cancelled and changing
environmental conditions that affect the acoustic field. An
Adaptive algorithm used to optimize the FIR or IIR filter
weights “on-line” in active noise and vibration control
systems are essentially derivations of the adaptive algorithms
used in systems such as telephone echo-cancellers and
adaptive optics in telescopes to cancel unwanted optical                       Figure 1. The general structure of an Adaptive Filter
“noise” and thus enhance signals from distant stars. One
                                                                        A. Related Work
such algorithm is Least Mean Square algorithm (LMS). The
LMS (least mean squares) algorithm is a member of stochastic               From the retrospective consideration it can be said that
gradient algorithms and approximation of the steepest descent           scads of work have been done in the domain of Realtime
algorithm which uses an instantaneous estimate of the                   ANC. Till now live audio data(input) corrupted by broadband
gradient vector of a cost function. The estimate of the gradient        noise have not yet been treated for filtration through adaptive
is based on sample values of the tap-input vector and an                modus in Simulink environment or using Data Acquisition
error signal. The initial idea is to use the LMS (Least-Mean-           Toolbox. But almost every time [see [5],[6],[7]] live audio
Square) algorithm to develop an adaptive filter that can be             processing (be it noise cancellation or some other) has been
© 2012 ACEEE                                                       26
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ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 02, March 2012


attempted and successfully performed using Embedded DSP             nature of interaction with the user. Stateflow is another entity
environment only. Simulink and DAQ toolbox have never               which is a part of Simulink environment. The Stateflow block
been directly used in true sense for handling live streaming        is a masked Simulink model. Stateflow builds an S-function
audio input in context of Active Noise cancellation through         that corresponds to each Stateflow machine. This S-function
adaptive filters. The novel approach followed in this               is the agent Simulink interacts with for simulation and
newspaper is very simple, direct and specially focussed to          analysis. Due to complex behaviour of S-function, any
enable even a novice to accomplish DSP chores of certain            realtime simulation experiences a bit of latency in its
complexity level with live realtime audio using Simulink as         performance. For true to form Realtime simulations MATLAB
well as Data Acquisition toolbox and basic MATLAB                   has developed an equipment to Simulink which is called Real-
commands. This method requires only winsound audio                  Time Workshop(now Simulink Coder). The Real-Time
adapter(present in all multimedia computers) for audio              Workshop product generates and executes stand-alone C/
acquisition unlike other data Acquisition techniques which          C++ codes for developing and testing algorithms modelled
may require expensive sophisticated sound adapters.                 in Simulink and Embedded MATLAB code. The resulting
                                                                    code is simpler than S functions and can be used for many
     II. REAL-TIME DIGITAL SIGNAL PROCESSING                        real-time and non-real-time applications, including simulation
                                                                    acceleration, rapid prototyping, and hardware-in-the-loop
    In realtime DSP, the real world (analog) data is input
                                                                    testing. You can tune and monitor the generated code using
through input transducers , conditioned and converted to
                                                                    Simulink blocks and built-in analysis capabilities, or run and
digital data via an ADC. The Digital Processor processes it
                                                                    interact with the code outside the MATLAB and Simulink
and DAC converts output of processor to analog data which
                                                                    environment.
is then conditioned through reconstruction filter and finally
                                                                        But getting a hand with Realtime Workshop and actually
supplied out of the system via output actuators. One
                                                                    working on it is tough for the beginners in MATLAB and
mandatory condition of the Real-Time DSP system is that the
                                                                    Simulink society. So simple Simulink Models with realtime I/
processing time must be less than the data transmission time.
                                                                    O and processing capabilities can be efficient to begin with
Real-time signal processing is generally done for animated
                                                                    especially for non advanced users such as students.
and continuous data such as audio, video and data streams
                                                                    1) Simulink Model For Noise Cancellation
where as batch processing (even though also employed for
stored audio and video data) is used for static data clusters
like image, files etc. There are three structural levels of real-
time processing briefed as follows
       Stream processing : all computations with one input
          sample are completed before the next input sample
          arrives
       Block processing : each input sample is stored in
          memory before any processing occurs upon it. After
          a bunch of input samples have arrived, the entire
          collection of samples is processed at once.
       Vector processing : systems with several input and/
          or output signals being computed at once: can work
          with streams or blocks
Further in this paper scope and purpose of Active Noise
Cancellation technique using Adaptive Filtering has been
experimented and explored in context of Simulink and DAQ                Figure 2. Simulink Module for FFT and Spectrogram output
Toolbox interface.
A. Implementation Using Simulink
   Simulink, [3]developed by MathWorks, is a commercial
tool for modelling, simulating and analyzing multidomain
dynamic systems. Its primary interface is a graphical block
diagramming tool and a customizable set of block libraries. It
offers tight integration with the rest of the MATLAB
environment and can either drive MATLAB or be scripted
from it. Simulink is widely used in control theory and digital
signal processing for multidomain simulation and Model-
Based Design. After defining a model one can integrate it
using a choice of integration methods either from Simulink
menus or by entering commands in MATLAB’s command
window. Simulations can be of the realtime or non realtime in                      Figure 3. Acoustic Noise Canceller
© 2012 ACEEE                                                   27
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ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 02, March 2012


2) Simulink Model Description
    The Simulink Model illustrated here consists of two
modules . Figure 2 module uses the Analog Input block to
acquire live signals from a data acquisition device(‘winsound’
adapter and microphone) into Simulink. The sound card is
selected as the input device in the Analog Input block. The
Simulink model performs spectral estimation through
periodogram method. A periodogram computes the spectral
content of a signal over time. The input signal is a real-time
audio through a microphone which is buffered into frames,
with 128 samples per frame. Each frame is then windowed
using a Hamming window function, and this blockset
computes the FFT for the windowed frame. The system                     Figure 4. Input Signal, Noise Corrupted Signal & Filtered Signal
collects the FFTs for successive frames and plots them to
produce a spectrogram. Even though a Windows sound card
was used for this demo, this model can be easily updated to
connect models to other supported data acquisition devices.
This provides the flexibility to use the same Simulink model
with different data acquisition device.
    Figure 3 module is the Acoustic Noise Canceller. This
module uses the acquired Signal from Analog Input block i.e.
data acquisition device in a real-time manner. Running FFT
of this signal is displayed using above module. Then this
signal is summed up with white noise from uniform noise
generator and fed to the Input port of LMS Adaptive Filter
block. White noise directly from noise generator is plugged
into the desired Signal port of the LMS Filter Block. Filter
length of LMS block is 60 and Sign-Data variant of LMS                  Figure 5. Spectrogram of Input(left) and Filtered(right) Signal
Algorithm is used for adaptive processing. Step size of LMS
Adaptive filter is switched between higher constant value             B. Implementation Using Data Acquisition Toolbox[4]
for fast but less accurate adaptation and lower constant value            Data Acquisition Toolbox™ provides a complete set of
for slower but more precise performance through manual                tools for analog input, analog output, and digital I/O from a
switch. The difference of desired Signal and Input Signal is          variety of PC-compatible data acquisition hardware. The
the error Signal which is obtained from error Signal output           toolbox lets you configure your external hardware devices,
port of LMS block. The Signal from this port is the Filtered          read data into MATLAB® and Simulink® for immediate
Signal from which Noise has been adaptively removed out.              analysis, and send out data.
Signal from output port is the Noise corrupted Signal. Sample         1) DAQ Procedure
based signals from these two output ports are supplied to                 First of all create(declare) analog input object and figure.
the Analog Output Block which delivered the acquired                  Then using ‘set’ command update userdata of figure and
samples to Audio Output device of the System in frame based           analog input. Now create(define) the analog input object and
manner. Single Output Device block is used and signals from           get first Adaptive output in the subsequent function.
output ports of LMS blocks are switched manually. Output              Establish a field in the object to collect batch/block of data
from weights port is down sampled 32 times and adaptation             samples . The length of this block/batch can be optimized
in the weights is viewed from Waterfall graph in time bound           (for performance) by setting proper values of ‘SampleRate’,
manner. Simulation only works fine in normal mode with                ’SamplesperTrigger’, ’Triggertype’, ’TriggerRepeat’ and
simulation stop time set to Infinity (optional).                      ‘TimerPeriod’ properties of the Data Acquisition object(ai) in
3) Scope Output                                                       accordance with each other and input signal.
    Figure 4 is the scope output of Simulink Model. In this               Create a function for display figures and plots. After
scope the top axes depicts pure audio signal entering the             storing handles in the data matrix configure callback to update
system. Second axes shows the resultant signal in voltage             the display. In the callback function any audio processing
coordinates after getting corrupted by Uniform Noise thus             algorithm can be written. This algorithm will be applied to
generated. The bottom axes is the signal obtained after               every block/batch of data samples acquired and stored in the
Adaptive filtering.                                                   internal buffer following every trigger. Output of the algorithm
                                                                      will update the previous output after every fixed interval(set
                                                                      on ‘TimerPeriod’ property of ‘ai’ object). This refresh period
                                                                      if carefully selected by managing trade-off between expected
                                                                      input device latency and processor speed, a live realtime
© 2012 ACEEE                                                     28
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audio processing can be performed and thus visualised using
line plots. The last step to be carried in the callback function
is to link the plots with triggered input sequence of samples
and also with the data batch/block output from algorithm.
Error Normalised Step Size LMS[2] algorithms is used for
adaptive filtering in the DAQ method. For the purpose of
reference, demoai_fft.m was used to understand and
implement the procedure hereby mentioned. This file can be
found in Data Acquisition Demos in MATLAB help having
label of “ FFT Display of an Incoming Signal”.
2) Error Normalised Step Size LMS(ENSSLMS)
Algorithm[2]
    In the ENSS proposed algorithm, the variable step is                    Figure 7. Input Signal, Noise Corrupted Signal & Filtered Signal
inversely proportional to the squared norm of the error vector.
                                                                        3) Figure Scope output
The length of the error vector is equal to the instantaneous
                                                                           The above Figure 7 demonstrates the real-time plot of
number of iterations. Thus, the weight update equation of
                                                                        Noise Cancellation using Data acquisition toolbox commands
the ENSS becomes
                                                                        and ENSS-LMS[2] adaptive filter . In this figure top axes
                                                                        shows the pure input signal acquired via acquisition device.
                                                                        The middle axes is the noise corrupted plot of above signal.
where                                                                   Audio signal after adaptive filtering is shown in the bottom
                                                                        axes.

is the squared norm of the error e(n), estimated over its entire                 III. DISCUSSION OF OUTPUTS/RESULTS
updated length and is the step size.
                                                                            In oscilloscope output from Simulink model one can see
                                                                        an elegant result. The noise is almost completely removed.
                                                                        The SNR for noise corrupted signal is 0-5 dB and post filtration
                                                                        SNR is approx 40 dB. Note the magnification of Y axis for
                                                                        middle plot is 1000x compared to top and bottom plots. Error
                                                                        convergence rate for every 4000 samples is 50-55 dB is also
                                                                        clear from Filtered Signal output of Figure 7 in left to right
                                                                        decrease of noise (disturbance) magnitude. Random noise of
                                                                        fixed variance (0.1) was generated in an internal function and
                                                                        summed up with the incoming system acquired audio. Average
                                                                        SNR calculated was 20dB. Using tic-toc function, time elapsed
                                                                        for entire callback function was found to be 0.01 to 0.015
                                                                        seconds. So the plot update time can be adjusted form
                                                                        anything to minimum of 0.02 seconds. Note that the length of
   Figure 6. Evolution of EMSE(Excess Mean Square Error) for            y axis of middle plot is twice that of top and bottom plots.
               ENSSLMS with identical set of inputs
The plot in Figure 6 shows the convergence rate of error                          IV. APPLICATION AND FUTURE SCOPE
signal (EMSE) when Noise Cancellation is carried out with
Adaptive Filter , designed using ENSS Algorithm. The single                 This method of data acquisition and signal processing
block length processed during interval of every callback loop           can be used for any kind of live audio stream and complex
is of 4096 samples. From the convergence plot (similar set of           signal processing algorithms can be tested on them in Realtime
signals were used in simulation as well as realtime                     manner. The Simulink method is much slower than DAQ
experimentation) , its visible that convergence rate is 50-55           method from realtime perspective. To improve this scene
dB for initial 4000 samples which is quite acceptable for               Simulink Coder (earlier called Real Time workshop) can be
practical purposes(exercised in the paper).                             used which generates and executes C & C++ code from
                                                                        Simulink model. The generated code can be used for Realtime
                                                                        Applications with much more efficiency and much less
                                                                        latency.




© 2012 ACEEE                                                       29
DOI: 01.IJCSI.03.02. 69
ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 02, March 2012


                         CONCLUSIONS                                    [2] Z. Ramadan and A. Poularikas, “An adaptive noise canceler
                                                                        using error nonlinearities in the LMS adaptation,” in Proc. IEEE
   This paper aimed at performing Realtime active nose                  Southeast Con 2004, Mar. 2004, vol. 1, pp. 359–364.
cancellation using Adaptive Filtering Technique through                 [3] Simulink, MATLAB version R2010a
Simulink Environment and DAQ toolbox commands. The aim                  [4] Data Acquisition Toolbox, MATLAB version R2010a.
was thus suitable fulfilled and basic level feasibility of              [5] R. Martínez, A. Álvarez, V. Nieto, V. Rodellar and P. Gómez,
Realtime Signal Processing using both the techniques was                “Implementation Of An Adaptive Noise Canceller On TMS320C
hereby demonstrated upto acceptable level of performance.               31-50 for Non Stationary Environments”, in Proceedings on the
The structural level of Realtime processing employed in this            13 th International Conference on Digital Signal Processing, Santorini,
paper was block-vectored type.                                          Greece, 2-4 July, 1997, pp. 49-52
                                                                        [6] Cristina Gabriela Saracin, Marin Saracin, Mihai Dascalu, Ana-
                                                                        Maria Lepar, “Echo Cancellation Using The LMS Algorithm”, in
                         REFERENCES                                     U.P.B. Sci. Bull., Series C, Vol. 71, Iss. 4, 2009, pp. 167-174
[1] Gaurav Saxena, Subramaniam Ganesan and Manohar Das,                 [7] Bambang Riyanto, “Real-time DSP Implementation of Active
“Real-time Implementation of Adaptive Noise Cancellation”, in           Noise Control for Broadband Noise Using Adaptive LMS Filter
IEEE International Conference on Electro/Information Technology,        Algorithm”, in Proceedings of the International Conference on
2008. EIT 2008, pp. 431-436, 27 June 2008                               Electrical Engineering and Informatics Institut Teknologi Bandung,
                                                                        Indonesia June 17-19, 2007, pp. 718-722




© 2012 ACEEE                                                       30
DOI: 01.IJCSI.03.02.69

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Real-Time Active Noise Cancellation with Simulink and Data Acquisition Toolbox

  • 1. ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 02, March 2012 Real-Time Active Noise Cancellation with Simulink and Data Acquisition Toolbox Vaibhav Narula1, Mukul Sagar2, Pranab Joshi2,Puneet S. Mehta2,Sudhanshu Tripathi2 1 Amity School of Engineering and Technology(Electronics and Communication), New Delhi, India Email: vaibhav3589@gmail.com 2 Amity School of Engineering and Technology(Electronics and Communication), New Delhi, India Email: {mukulsagar1989, joshi.pranab04, puneetmehta90, sudhanshutripathi14}@gmail.com Abstract— This paper presents the feasibility of implementing used in ANC (Active Noise Cancellation) applications. The single channel negative feedback Active Noise Cancellation method uses a noisy signal as primary input and a reference technique using adaptive filters in Real-time environment[1]. input that consists of noise correlated in some arbitrary manner In order to establish the suitability and credibility of LMS with the primary noise. By adaptively filtering and subtracting Algorithm for adaptive filtering in real world scenario, its the reference input from the primary input, the output of the efficiency was tested beyond system based ideal simulations. Within the MATLAB® software environment two different adaptive filter will be the error signal, which acts as a feedback methods were used to perform Real-time ANC namely to the adaptive filter. With this setup, the adaptive filter will Simulink® and Data Acquisition ToolboxTM. Human voice is be able to cancel the noise and obtain a less noisy signal used as test signal. For processing and performing adaptive estimate. Adaptive filter is generally defined as a filter whose filtering, Block LMS Filter was utilised in Simulink and Error characteristics can be modified to achieve some objective Normalised Step Size algorithm was used in between input and is usually assumed to accomplish this adaptation and output of Signals by DAQ (Data Acquisition) toolbox automatically, without the need for substantial intervention interface. A general method of using DAQ commands has been by user. Here, an adaptive filter is needed that is capable of employed which also allows for almost any kind of complex altering its impulse response as required for the noise real-time audio processing and is quite easy to follow. cancellation application. Figure 1 shows the general structure Index Terms— Active Noise Cancellation, Adaptive Filters, of an adaptive filter. Figure 1 is composed of three blocks as LMS, ENSSLMS, Simulink, Data Acquisition, Real-time shown. The first block is Adaptive filter block which shows what type of digital filter is used in the design of the adaptive I. INTRODUCTION filter. The second block depicts filter adaptation rules which show the updating algorithm used for the adaptive filter to Active noise cancellation is basically the electro-acoustic update its parameters, and last block as its name shows generation of a sound field to cancel an unwanted existing performs the quality assessment or simply comparison in an sound field. The active noise cancellation system is known adaptive system, the output of this block is the error signal as an “adaptive” system as it can adapt itself to changing which is fed back to filter adaptation rules block to find new characteristics of the noise to be cancelled and changing parameters. In Figure 1, the filter could be implemented in environmental conditions that affect the acoustic field. An analogue or digital, which here a digital filter is used. Active noise cancellation is basically the electro-acoustic generation of a sound field to cancel an unwanted existing sound field. The active noise cancellation system is known as an “adaptive” system as it can adapt itself to changing characteristics of the noise to be cancelled and changing environmental conditions that affect the acoustic field. An Adaptive algorithm used to optimize the FIR or IIR filter weights “on-line” in active noise and vibration control systems are essentially derivations of the adaptive algorithms used in systems such as telephone echo-cancellers and adaptive optics in telescopes to cancel unwanted optical Figure 1. The general structure of an Adaptive Filter “noise” and thus enhance signals from distant stars. One A. Related Work such algorithm is Least Mean Square algorithm (LMS). The LMS (least mean squares) algorithm is a member of stochastic From the retrospective consideration it can be said that gradient algorithms and approximation of the steepest descent scads of work have been done in the domain of Realtime algorithm which uses an instantaneous estimate of the ANC. Till now live audio data(input) corrupted by broadband gradient vector of a cost function. The estimate of the gradient noise have not yet been treated for filtration through adaptive is based on sample values of the tap-input vector and an modus in Simulink environment or using Data Acquisition error signal. The initial idea is to use the LMS (Least-Mean- Toolbox. But almost every time [see [5],[6],[7]] live audio Square) algorithm to develop an adaptive filter that can be processing (be it noise cancellation or some other) has been © 2012 ACEEE 26 DOI: 01.IJCSI.03.02.69
  • 2. ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 02, March 2012 attempted and successfully performed using Embedded DSP nature of interaction with the user. Stateflow is another entity environment only. Simulink and DAQ toolbox have never which is a part of Simulink environment. The Stateflow block been directly used in true sense for handling live streaming is a masked Simulink model. Stateflow builds an S-function audio input in context of Active Noise cancellation through that corresponds to each Stateflow machine. This S-function adaptive filters. The novel approach followed in this is the agent Simulink interacts with for simulation and newspaper is very simple, direct and specially focussed to analysis. Due to complex behaviour of S-function, any enable even a novice to accomplish DSP chores of certain realtime simulation experiences a bit of latency in its complexity level with live realtime audio using Simulink as performance. For true to form Realtime simulations MATLAB well as Data Acquisition toolbox and basic MATLAB has developed an equipment to Simulink which is called Real- commands. This method requires only winsound audio Time Workshop(now Simulink Coder). The Real-Time adapter(present in all multimedia computers) for audio Workshop product generates and executes stand-alone C/ acquisition unlike other data Acquisition techniques which C++ codes for developing and testing algorithms modelled may require expensive sophisticated sound adapters. in Simulink and Embedded MATLAB code. The resulting code is simpler than S functions and can be used for many II. REAL-TIME DIGITAL SIGNAL PROCESSING real-time and non-real-time applications, including simulation acceleration, rapid prototyping, and hardware-in-the-loop In realtime DSP, the real world (analog) data is input testing. You can tune and monitor the generated code using through input transducers , conditioned and converted to Simulink blocks and built-in analysis capabilities, or run and digital data via an ADC. The Digital Processor processes it interact with the code outside the MATLAB and Simulink and DAC converts output of processor to analog data which environment. is then conditioned through reconstruction filter and finally But getting a hand with Realtime Workshop and actually supplied out of the system via output actuators. One working on it is tough for the beginners in MATLAB and mandatory condition of the Real-Time DSP system is that the Simulink society. So simple Simulink Models with realtime I/ processing time must be less than the data transmission time. O and processing capabilities can be efficient to begin with Real-time signal processing is generally done for animated especially for non advanced users such as students. and continuous data such as audio, video and data streams 1) Simulink Model For Noise Cancellation where as batch processing (even though also employed for stored audio and video data) is used for static data clusters like image, files etc. There are three structural levels of real- time processing briefed as follows  Stream processing : all computations with one input sample are completed before the next input sample arrives  Block processing : each input sample is stored in memory before any processing occurs upon it. After a bunch of input samples have arrived, the entire collection of samples is processed at once.  Vector processing : systems with several input and/ or output signals being computed at once: can work with streams or blocks Further in this paper scope and purpose of Active Noise Cancellation technique using Adaptive Filtering has been experimented and explored in context of Simulink and DAQ Figure 2. Simulink Module for FFT and Spectrogram output Toolbox interface. A. Implementation Using Simulink Simulink, [3]developed by MathWorks, is a commercial tool for modelling, simulating and analyzing multidomain dynamic systems. Its primary interface is a graphical block diagramming tool and a customizable set of block libraries. It offers tight integration with the rest of the MATLAB environment and can either drive MATLAB or be scripted from it. Simulink is widely used in control theory and digital signal processing for multidomain simulation and Model- Based Design. After defining a model one can integrate it using a choice of integration methods either from Simulink menus or by entering commands in MATLAB’s command window. Simulations can be of the realtime or non realtime in Figure 3. Acoustic Noise Canceller © 2012 ACEEE 27 DOI: 01.IJCSI.03.02. 69
  • 3. ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 02, March 2012 2) Simulink Model Description The Simulink Model illustrated here consists of two modules . Figure 2 module uses the Analog Input block to acquire live signals from a data acquisition device(‘winsound’ adapter and microphone) into Simulink. The sound card is selected as the input device in the Analog Input block. The Simulink model performs spectral estimation through periodogram method. A periodogram computes the spectral content of a signal over time. The input signal is a real-time audio through a microphone which is buffered into frames, with 128 samples per frame. Each frame is then windowed using a Hamming window function, and this blockset computes the FFT for the windowed frame. The system Figure 4. Input Signal, Noise Corrupted Signal & Filtered Signal collects the FFTs for successive frames and plots them to produce a spectrogram. Even though a Windows sound card was used for this demo, this model can be easily updated to connect models to other supported data acquisition devices. This provides the flexibility to use the same Simulink model with different data acquisition device. Figure 3 module is the Acoustic Noise Canceller. This module uses the acquired Signal from Analog Input block i.e. data acquisition device in a real-time manner. Running FFT of this signal is displayed using above module. Then this signal is summed up with white noise from uniform noise generator and fed to the Input port of LMS Adaptive Filter block. White noise directly from noise generator is plugged into the desired Signal port of the LMS Filter Block. Filter length of LMS block is 60 and Sign-Data variant of LMS Figure 5. Spectrogram of Input(left) and Filtered(right) Signal Algorithm is used for adaptive processing. Step size of LMS Adaptive filter is switched between higher constant value B. Implementation Using Data Acquisition Toolbox[4] for fast but less accurate adaptation and lower constant value Data Acquisition Toolbox™ provides a complete set of for slower but more precise performance through manual tools for analog input, analog output, and digital I/O from a switch. The difference of desired Signal and Input Signal is variety of PC-compatible data acquisition hardware. The the error Signal which is obtained from error Signal output toolbox lets you configure your external hardware devices, port of LMS block. The Signal from this port is the Filtered read data into MATLAB® and Simulink® for immediate Signal from which Noise has been adaptively removed out. analysis, and send out data. Signal from output port is the Noise corrupted Signal. Sample 1) DAQ Procedure based signals from these two output ports are supplied to First of all create(declare) analog input object and figure. the Analog Output Block which delivered the acquired Then using ‘set’ command update userdata of figure and samples to Audio Output device of the System in frame based analog input. Now create(define) the analog input object and manner. Single Output Device block is used and signals from get first Adaptive output in the subsequent function. output ports of LMS blocks are switched manually. Output Establish a field in the object to collect batch/block of data from weights port is down sampled 32 times and adaptation samples . The length of this block/batch can be optimized in the weights is viewed from Waterfall graph in time bound (for performance) by setting proper values of ‘SampleRate’, manner. Simulation only works fine in normal mode with ’SamplesperTrigger’, ’Triggertype’, ’TriggerRepeat’ and simulation stop time set to Infinity (optional). ‘TimerPeriod’ properties of the Data Acquisition object(ai) in 3) Scope Output accordance with each other and input signal. Figure 4 is the scope output of Simulink Model. In this Create a function for display figures and plots. After scope the top axes depicts pure audio signal entering the storing handles in the data matrix configure callback to update system. Second axes shows the resultant signal in voltage the display. In the callback function any audio processing coordinates after getting corrupted by Uniform Noise thus algorithm can be written. This algorithm will be applied to generated. The bottom axes is the signal obtained after every block/batch of data samples acquired and stored in the Adaptive filtering. internal buffer following every trigger. Output of the algorithm will update the previous output after every fixed interval(set on ‘TimerPeriod’ property of ‘ai’ object). This refresh period if carefully selected by managing trade-off between expected input device latency and processor speed, a live realtime © 2012 ACEEE 28 DOI: 01.IJCSI.03.02.69
  • 4. ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 02, March 2012 audio processing can be performed and thus visualised using line plots. The last step to be carried in the callback function is to link the plots with triggered input sequence of samples and also with the data batch/block output from algorithm. Error Normalised Step Size LMS[2] algorithms is used for adaptive filtering in the DAQ method. For the purpose of reference, demoai_fft.m was used to understand and implement the procedure hereby mentioned. This file can be found in Data Acquisition Demos in MATLAB help having label of “ FFT Display of an Incoming Signal”. 2) Error Normalised Step Size LMS(ENSSLMS) Algorithm[2] In the ENSS proposed algorithm, the variable step is Figure 7. Input Signal, Noise Corrupted Signal & Filtered Signal inversely proportional to the squared norm of the error vector. 3) Figure Scope output The length of the error vector is equal to the instantaneous The above Figure 7 demonstrates the real-time plot of number of iterations. Thus, the weight update equation of Noise Cancellation using Data acquisition toolbox commands the ENSS becomes and ENSS-LMS[2] adaptive filter . In this figure top axes shows the pure input signal acquired via acquisition device. The middle axes is the noise corrupted plot of above signal. where Audio signal after adaptive filtering is shown in the bottom axes. is the squared norm of the error e(n), estimated over its entire III. DISCUSSION OF OUTPUTS/RESULTS updated length and is the step size. In oscilloscope output from Simulink model one can see an elegant result. The noise is almost completely removed. The SNR for noise corrupted signal is 0-5 dB and post filtration SNR is approx 40 dB. Note the magnification of Y axis for middle plot is 1000x compared to top and bottom plots. Error convergence rate for every 4000 samples is 50-55 dB is also clear from Filtered Signal output of Figure 7 in left to right decrease of noise (disturbance) magnitude. Random noise of fixed variance (0.1) was generated in an internal function and summed up with the incoming system acquired audio. Average SNR calculated was 20dB. Using tic-toc function, time elapsed for entire callback function was found to be 0.01 to 0.015 seconds. So the plot update time can be adjusted form anything to minimum of 0.02 seconds. Note that the length of Figure 6. Evolution of EMSE(Excess Mean Square Error) for y axis of middle plot is twice that of top and bottom plots. ENSSLMS with identical set of inputs The plot in Figure 6 shows the convergence rate of error IV. APPLICATION AND FUTURE SCOPE signal (EMSE) when Noise Cancellation is carried out with Adaptive Filter , designed using ENSS Algorithm. The single This method of data acquisition and signal processing block length processed during interval of every callback loop can be used for any kind of live audio stream and complex is of 4096 samples. From the convergence plot (similar set of signal processing algorithms can be tested on them in Realtime signals were used in simulation as well as realtime manner. The Simulink method is much slower than DAQ experimentation) , its visible that convergence rate is 50-55 method from realtime perspective. To improve this scene dB for initial 4000 samples which is quite acceptable for Simulink Coder (earlier called Real Time workshop) can be practical purposes(exercised in the paper). used which generates and executes C & C++ code from Simulink model. The generated code can be used for Realtime Applications with much more efficiency and much less latency. © 2012 ACEEE 29 DOI: 01.IJCSI.03.02. 69
  • 5. ACEEE Int. J. on Control System and Instrumentation, Vol. 03, No. 02, March 2012 CONCLUSIONS [2] Z. Ramadan and A. Poularikas, “An adaptive noise canceler using error nonlinearities in the LMS adaptation,” in Proc. IEEE This paper aimed at performing Realtime active nose Southeast Con 2004, Mar. 2004, vol. 1, pp. 359–364. cancellation using Adaptive Filtering Technique through [3] Simulink, MATLAB version R2010a Simulink Environment and DAQ toolbox commands. The aim [4] Data Acquisition Toolbox, MATLAB version R2010a. was thus suitable fulfilled and basic level feasibility of [5] R. Martínez, A. Álvarez, V. Nieto, V. Rodellar and P. Gómez, Realtime Signal Processing using both the techniques was “Implementation Of An Adaptive Noise Canceller On TMS320C hereby demonstrated upto acceptable level of performance. 31-50 for Non Stationary Environments”, in Proceedings on the The structural level of Realtime processing employed in this 13 th International Conference on Digital Signal Processing, Santorini, paper was block-vectored type. Greece, 2-4 July, 1997, pp. 49-52 [6] Cristina Gabriela Saracin, Marin Saracin, Mihai Dascalu, Ana- Maria Lepar, “Echo Cancellation Using The LMS Algorithm”, in REFERENCES U.P.B. Sci. Bull., Series C, Vol. 71, Iss. 4, 2009, pp. 167-174 [1] Gaurav Saxena, Subramaniam Ganesan and Manohar Das, [7] Bambang Riyanto, “Real-time DSP Implementation of Active “Real-time Implementation of Adaptive Noise Cancellation”, in Noise Control for Broadband Noise Using Adaptive LMS Filter IEEE International Conference on Electro/Information Technology, Algorithm”, in Proceedings of the International Conference on 2008. EIT 2008, pp. 431-436, 27 June 2008 Electrical Engineering and Informatics Institut Teknologi Bandung, Indonesia June 17-19, 2007, pp. 718-722 © 2012 ACEEE 30 DOI: 01.IJCSI.03.02.69