ELEC4123 – Electrical Design Proficiency

Laboratory 2: Signal Processing

/Electronic Systems Design

Group Members: Bryan Loh (z3444283) Floyd D’Souza (z3437815)

Lab day/time/class: Wednesday, 09:00 – 13:00, Elec Eng 101

Lab Group/Bench Number: 102-C1

Semester/Year: Semester 1, 2015

Aim:

To turn-on and off an LED based on singular (on) or consecutive (off) claps, with a high degree of

reliability, robustness and tolerance to noise.

Introduction

- Detailed Requirements:

This particular design task requires the implementation of an analogue circuit or digital code in

order to correctly process and interpret the signal to fulfil the following:

True requirements:

1. One clap should turn the light on and two claps in quick succession should turn it off

2. The system’s operation should be robust and reliable (especially to ambient noise)

3. If using the computer, the sampling rate should be minimised at the input

Desirable Requirements:

1. Larger the distance between the user and microphone that the system can work at 2. Higher robustness to noise

Methods to determine a peak

Envelope detector

An envelope detector is an electronic circuit that takes a high-frequency signal as input and provides an output, which is the envelope of the original signal. The capacitor in the circuit stores up charge on the rising edge, and releases it slowly through the resistor when the signal falls. This technique is very simple and cheap, with low distortion. It is appropriate for this design task because it provides more definite peaks which can both be detected easier, and characterised by its shape more efficiently to determine a clap specifically. Before giving the circuit an input, the signal must be band pass filtered otherwise the circuit will demodulate unnecessary signals.

Windowing

This technique allows segmented parts of the entire value array to be analysed based on the rate at which the system is receiving information, and how much of it. The technique is particularly adequate for this project as samples can be taken in real time through a ‘loop’ in Matlab and compared to pre and post windows, making the analysis of the time varying signal much simpler. The window used in this case is a simple rectangular window. The size of each window can be determined by 2 key characteristics:

- The time between each clap; in this case a size of 0.2s was used (2000 samples), and; - The maximum number of windows (time taken) for consecutive claps, which was max 3

windows, at approximately 0.6seconds.

The only issue with this technique is that distortion in the signal being analysed, can cause peaks to overlap and parts of the peak to appear on the edge of one window and the next, incorrectly identifying 2 peaks in 2 windows when only 1 was found. A Hamming window could have been used instead to minimise distortion due to its smoother shape, but given the time constraints, was avoided as it is harder to implement.

Sound Characteristics The challenge with identifying a clap is that there a many other sounds such as a finger click or a foot step on a hard surface that results in a high intensity peak in amplitude in the time domain similar to that of a clap which will be the fundamental shape we will focus on. To be able to differentiate between a clap, the differences within the actual sound rather than shape of the sound can be a telling difference. The following differences in sound include:

Pitch: How high or low a sound/note is. Higher up in pitch a particular sound is, higher the frequency and vice versa lower the pitch, lower the frequency. At a fixed distance, the pitch of these sounds are quite distinct, however care must be taken when analysing the frequency of the sound as closer the sound is to the mic, the larger the frequency of the sound wave due to the Doppler effect, clouding the sound characteristic

Timbre/Tone Colour/Envelope/Spectrum: A sound that distinguishes different types of sound production such as different types of instruments. In the picture below, the 6 instruments contains the same pitch and amplitude, but very different timbre/envelope shape to distinguish between them, largely based around the sounds time variation and spectral harmonic content in the frequency domain. Time Domain

Frequency Domain

Duration: As a clap reverberates a bit more (tens of micro seconds range) than a finger click, and shorter than a stomp on the ground, this may affect the duration of the voltage spike1

The only other sound property there is but can’t be used in the determination of a clap (from other similar sounds) is amplitude due to the variability in claps from age, sex, position of clap on hand etc.

Operation of the light For this task, we will use a linear current source or open-collector circuit as depicted in the circuit on the right. When the user claps once, the DAQ output a HI (>0.7V) to the base of the transistor, in this situation the transistor is now ON, causing current to flow from Vcc to ground. When the user claps twice, the DAQ outputs zero, this isn’t enough voltage to forward-bias the transistor and the transistor is off causing the LED to be at the same potential as Vcc as there is no ground connection, therefore no current flows through the circuit. As the display (LED circuit) of our signal processing is not as important for this task as the signal processing coding or circuitry itself, this design will suffice for this task. In practice and real world application, this driver could be done more efficiently, either using a MOSFET in accordance with the DAQ to control its gate, with minimal if not no current required, biasing the MOSFET meaning a very low input power for the necessary output power, therefore high efficiency system. Another option available for a real practical application would be using a switch mode current source which provides near constant current and therefore regulates the voltage using a converter and automatic adjustment of the duty cycle, to account for the losses and dimming of the LED over time, a very efficient solution. This circuit is more complex and not necessary for this small task.

Robust solution The definition of robustness for this project is that with different clapping sequences and background noises that the system works as it is supposed to. In order to distinctly identify the signal being fed from the microphone into the DAQ, an amplifying circuit is connected between the 2, consisting of a resistor and capacitor (takes the output). Within the code itself, a lower pass filter aims to remove or at least dampen the high frequency components of the ambient noise, and the side intervals of the peak. Given the resultant noise reduction, a peak can be identified much more easily by setting a threshold values that defines a peak for all values above, with the lower the threshold value, the further away the clap can be from the mic and still be detectable.

1 http://www.animations.physics.unsw.edu.au/jw/sound-pitch-loudness-timbre.htm

Minimise Sampling Rate (Fs) By minimising the sampling rate, the energy used in the process is effectively decreased as less as less work is done in taking ‘X’ amount of samples in‘t’ seconds. Power conservation is always a valuable characteristic of a system, especially if the system runs on-going. In order to reduce the sampling frequency to a sufficient value determined by the designer and still have proper operation of the circuit, the number of samples taken in per window must therefore be reduced accordingly, therefore a smaller window. The window size however must be high enough such that a peak is visible across only 1 window frame and not multiple as it will be harder to differentiate. As a digital low pass filter is used, the filtering of higher frequencies would allow for a more smooth signal and thus a lower sampling frequency can be used.

Implementation and Test Plan:

The following table is a program for the project, providing a detailed description of the tasks that need to be completed in order to achieve the

objectives. This will increase productivity and lab efficiency, and ensure project completion within the given time whilst fulfilling the

requirements.

*Note, all formulas and results will be provided in the detailed design section to follow.

Task

No.

Task Action Comments

1 Measure

consecutive

claps and

window length

By looking at the response of consecutive claps in Matlab

through the DAQ, determine the time lapse between the

2 claps.

Do tests about 5 times to ensure consistency between

time delay

Find no. of samples in a window (window length) which

allocate 1 peak ONLY in 1 window

Do the 5 or 6 tests with different people as different people

might perform consecutive claps (naturally) at different speeds.

Looking at the maximum time lapse between gaps, will allow us

to differentiate between singular and consecutive claps.

Never want more than 1 peak in a window therefore must be

small and big enough so that this is the case

2 Design Matlab

Low-Pass filter,

test

Using first principles, implement a Low-Pass filter with the

cut-off frequency (<500Hz)

Using the signal generator, input signals with frequencies

greater than the cut-off frequency and to ensure correct

low pass operation.

No tool box functions can be used in Matlab except for the FFT

required to convert the time domain signal into its frequency

domain.

Low-Pass filter will also make the peaks of the sounds more

accurate with less staggered intervals (high frequency

components) on the way up to the absolute peak

3 Implement a

‘find peaks’

algorithm, test

Observe the signal in the time domain, looking for the

absolute peaks which correspond to a clap within the

given frequency spectrum. Setting some threshold

minimum value will stop residual noise peaks from being

picked up

Don’t use the windowing technique yet during this stage. Look

at a large set of values first.

This is only the first step of characterising a clap

Note that the size of the threshold value may limit the distance

between the user and the microphone for the system to still

Test a number of sounds, amplitudes and distances away

from the mic and observe their peaks. Determine a good

enough distance where the peak will stand out and set

the threshold value to that (min)

function properly.

4 Implement

window, test

Set window length based on ‘step 1’ data.

Set while loop to count to iterate (Fs*Time)/window

length times.

Each iteration (window) must search for a peak governed

by the set threshold value (i.e. just above noise levels)

Identify across how many windows at max, consecutive

(2) claps would go across (‘x’ boxes) given the window

length. ‘x’ number of windows will be used for logic

operations, whereby after a clap, ‘x’ boxes are checked to

check for another clap.

Based on the sampling frequency (Fs) multiplied by the number

of seconds the program will run for, this is the total amount of

samples gathered. The number of windows is

(Fs*Time)/window length.

There is a risks that searching for ‘x’ windows with (x*window

length) samples could take a long time to process the code,

affecting how fast the output turns on.

5 Implement logic,

test

Given a successful clap;

If another clap is found within the ‘x’ windows, consider it

consecutive claps and send a LO to be output by the DAQ

to the light

If the ‘x’ windows find no other peak, consider it a single

clap and send a HI to be output by the DAQ to the light

Test a number of clapping sequences to ensure the

system is robust

This step will require a counter to be established every time an

operation has been undertaken.

Provide multiple test cases that must perform the correct

operation with different clap sequences and times.

Ensure that the HI signal output from the DAQ is > 0.7V and a

low is less than 0.7V to maintain correct operation of the LED

driver circuit analysed next.

6 Implement

driver circuit,

test

If an open collector BJT is used as the LED driving circuit:

If DAQ outputs a HI (>0.7V) into the base of the transistor

If DAQ outputs a LO into the base of the transistor, the

transistor is off (<0.7V) into the base of the transistor

Re-do some of the tests from the plan to ensure correct

operation of the open collector driver circuit.

Ensure the voltage and current rating of the LED is adhered to

and use sufficient resistor in series with the LED.

7 Determine

characteristics

of a sound wave

and how they

change the

shape of the

waveform

(OPTIONAL)

Research the following sound characteristics and its effect on

the sound wave in the time and frequency domain:

- Pitch

- Tone

- Volume/loudness

These characteristics will form the basis of the envelope or shape of

the clap that we aim to achieve, differing from other sounds similar

in nature such as clicking of the fingers or stomping your foot

8 Determine the

sound

characteristics

of a clap

(OPTIONAL)

Research the above characteristics and frequency domain

specifically of a clap.

Will help determine the frequency band which we will need to

focus on (limits on a band pass filter) to increase robustness

from noise outside this range.

Using the sound characteristics from step 7, specific to a hand

clap, we can form an envelope, defining the characteristics that

will enable us to compare sounds through the microphone to

what we have defined as a clap, to then perform the correct

output operation.

9 Implement ‘clap

characterisation’

algorithm, test

(OPTIONAL)

Based on the knowledge of a clap, and different sound

wave characteristics of a clap and other similar sounds,

look at the clap enveloped waveform, identifying the clap

characteristics in turns of shape of turning point, gradient,

width etc.

Use this criterion to test for claps

This ‘clap characterisation’ will be the most difficult to

implement, as claps sound very similar to many other sounds.

There is a risk that the program may spend a lot of time

comparing the actual signal section Can try a ‘matched filter’

instead of detail comparison programming

All the operations done in this step is in the time domain

10 Implement

envelope, test

(OPTIONAL)

By using the configuration above, implement envelope

circuit

Test to insure that modulated waveform is smooth and

distinct

Detailed Design and Solution:

- Clap Detection and Logic

Our logic looks at ‘x’ number of windows in real-time to determine clap sequence. ‘x’ is defined by

the minimum number of windows that needs to be looked at sequentially to determine if it’s 1, 2,

3 etc. claps performed in succession. Setting Fs = 10kHz, and using 5000 samples per window,

when we clapped twice, often both clap peaks were encountered in 1 window, implying the

window was too wide. So decreasing the window size to 2000 samples, then clapping twice at

different speeds, at no stage was there 2 peaks to 1 window, and the 2 claps consistently spread

across either 2 or 3 windows. Therefore we could determine that 3 windows could be used as our

logic test case due to the consistency of clap results, at Fs = 10kHz and 2000 sample window.

These results are shown below of a single and double clap.

Given this, we could implement our logic that was able to distinguish between singles claps and

consecutive (>=2) claps as shown on the next page. The logic accounts for more than 2 consecutive

claps whereby it will keep the light off.

Window ‘i'

Yes

No

No

Yes

Yes No

i = i + 1

- Filtering

Because of the ambient noise being detected by the microphone alongside the claps, the signal

waveform is very noisy. Therefore, a filter is introduced to process the signal and filter out the

unwanted noise components. The filter implemented in this system is a second-order elliptic low

pass filter. This filter will smoothen out the noisy and unwanted high frequencies of the ambient

noise. The cut-off frequency was chosen at 200Hz as most ambient noise frequencies and its

harmonics were found to be in the kHz range. The filter design was done in MATLAB using

‘ellipord’, ‘ellip’ and ‘filter’ functions. The ‘ellipord’ function takes in the passband and stopband

edge frequencies together with the desired passband ripple and stopband attenuation and returns

a suitable order and elliptic natural frequency for the filter design. The ‘ellip’ function then takes

these values and returns the filter coefficients. Lastly, the ‘filter’ function is used to filter the signal

with the designed filter. Below is the comparison between the filtered and unfiltered clap signals:

Has a clap been found previously & 2

windows have now passed since then

Clap found (Window Max value > Threshold)

Is this the 1st clap found?

Save window number ‘found = i’

Consecutive clap Turn light OFF

Read in microphone values

Single Clap Turn light ON

Restart clap status

Figure 1: Unfiltered clap signal

Figure 2: Filtered clap signal

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20

0.05

0.1

0.15

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20

0.05

0.1

0.15

As seen, the filtered signal is much smoother compared to the unfiltered one. As such, the clap

peak is more distinct and easily detected. Because of this, the system is able to detect claps at a

much further distance than normal. Also, because the high frequencies have been filtered out, this

allows us to lower our sampling frequency from 15kHz to 10kHz with no effect on the system.

Circuit Design (Hardware)

Figure 3: Clapper circuit design

The circuit design is fairly straightforward for this system as most of the process is done via software. The electret microphone is first connected in a microphone biasing circuit, which consists of a resistor (2.2kΩ) and a capacitor (1µF). The output of the capacitor is then connected to the DAQ input for sampling and signal processing. The DAQ interfaces with MATLAB where most of the filtering and logic is carried out. The DAQ then outputs a logic signal via the output ports, into the base of the transistor. When the user claps once, the DAQ output a HI (5V) to the base of the transistor, in this situation the transistor is now ON, causing current to flow from Vcc to ground. When the user claps twice, the DAQ outputs zero, this is not enough voltage to forward-bias the transistor and the transistor is off causing the LED to be at the same potential as Vcc as there is no ground connection, therefore no current flows through the circuit.

Conclusion and Reflection:

In conclusion, the ‘clapper’ system achieved the design requirements. The LED switches on when a clap is detected and OFF when two claps are detected. Secondly, the system is robust to ambient noise including noises similar in nature to claps such as clicking fingers and tapping the table, as it has been solved with a second order low-pass filter. Lastly the sampling rate of the system was minimized to about 10kHz. Moreover, other desirable requirements have also been met, such as activating the clapper at large distances (8 meters being the largest so far, limited by the room

setup to try further distances), and it is highly robust to noise. Unfortunately, due to time constraints, we were unable to execute better ideas that would further improve the responsiveness and the robustness of this system, e.g. envelope detection, clap characterization through the analysis of sound properties, and the use of a Hamming window instead of a rectangular window. This lab had many challenges, which we managed to overcome, and also learned much in the process. The design task really assessed us on our proficiency in designing, interfacing and troubleshooting hardware and software and manipulating signals to perform respective actions. Due to the time constraint and limited resources, we learnt to be more organized and systematic in laying out a task plan, and executing specific tasks to better achieve our goal. We also learned to think more critically and analyze a problem more thoroughly, to find a solution and workaround. Moreover, this lab also taught us to be more particular about everything to the finest detail, as a small miscalculation could have big impacts on the system (e.g. tuning the low pass filter). Lastly, we would also like to thank our lecturer Dr. Aboutanios for teaching us to think more critically and exposing us to real-life working scenarios. Thoroughly enjoyable project.

Appendix A – Matlab code

close all; clear all; clc;

info = daqhwinfo('nidaq');

% Predefined values Fs = 10000; %sampling frequency found = 0; off = 0; seconds = 50; %test duration i = 0; samplesPerIteration = 2000;

% Setting up input channels ai = analoginput('nidaq','Dev2'); %input channels addchannel(ai,0:2) set(ai,'SampleRate',Fs) %sampling rate set(ai,'SamplesPerTrigger',seconds*Fs) ao = analogoutput('nidaq','Dev2'); %output channels addchannel(ao,0) start(ai); %start program

%Low-pass filter f1 = 200; %Cutoff frequency f2 = 300; %Stop band Ap = 0.5; %dB value at f1 (positive difference from 0dB - MUST BE POSITIVE VALUE) As = 20; %dB value at f2 (positive difference from 0dB - MUST BE POSITIVE VALUE) [N,wn] = ellipord(f1/(Fs/2), f2/(Fs/2),Ap,As); %N = order, wn = natural frequency

[B1,A1] = ellip(N,Ap,As,wn); % B and A are the coefficients of the digital filter

samples = seconds*Fs; totalIterations = samples/samplesPerIteration; %seconds*Fs = total number of smaples

while(i ~= totalIterations) acquiredData = getdata(ai, samplesPerIteration); %4000 = no. of samples data1 = acquiredData(:,1); % every sample from channel 1. data1 is of length 4*Fs y=abs(data1(1)-data1); %rectified. data1(1) may or may not start at 0, so

find each value relative to the first value y_lpf = filter(B1,A1,y); %data after lowpassfilter t=0:1/Fs:(length(y)-1)/Fs; %time if (max(y_lpf) > 0.011) if ((found == 0)); %1st peak in sequence found = i; %first peak in 3 box sequence found if(i == (totalIterations-1)) % account for clap in last window putsample(ao,5) %output HI

end else %next peak found, for consecutive claps putsample(ao,0) %turn off LED off = 1; found = i; end end

if (((i-found)== 2)&&(found~=0)) %Singular clap if (off == 0) %reset found after off state change putsample(ao,5) end found = 0; off = 0; end

figure(1); plot(t,y_lpf); %real time plot of low-pass filter data ylim([0 0.15]); xlabel('time'); ylabel('filtered voltage');

i = i+1; end putsample(ao,0)