Finance Upper Division
Solve and send answers numerically! Need it ASAP! Finance and Valuation. Advanced!
Matlab-Spice Project – EE-310
This is a practical project that uses circuitry and signal processing to analyze evidence for a jury
You must work in groups of two to four on this project. (Each group member should submit the
report individually, where the page after cover page should state the contribution each group
member.) Your report should have a professional appearance and be created in Microsoft Word
or LaTeX. You can also include handwritten equations in an appendix, but they must be properly
referenced and clearly written. Equations in the body of the report should be created with an
equation editor (e.g. https://codecogs.com/latex/eqneditor.php, or the one included in Word).
For this project you will need MATLAB and LTSpice. You can download a copy of LTSpice from
the Analog Devices website. MATLAB is available through the University. You may refer to the
separate tutorials given in the lecture on them for your references.
In this project, you are to serve as an expert witness in a courtroom trial. The defense has
submitted as evidence a 10-second audio snippet from a recorded telephone call. The prosecutor
suspects that the recording has been altered to add or omit words, and has hired you to review
the recording and assess its authenticity.
You suspect that the recording was made in an office environment lit by fluorescent lighting, and
experience tells you that these lights emit a weak 60 Hz humming sound that would be
captured on the recording. The amplitude of the captured 60 Hz hum will fluctuate with time,
but its phase should be perfectly continuous – unless, of course, the recording was altered. Your
strategy is to examine the phase trajectory of the 60 Hz hum by extracting it from the person
speaking and the ambient room noise. If the phase exhibits discontinuities, you will conclude
that the recording has been altered and is therefore tainted evidence.
The signal processing steps are shown in Figure 1. The input on the left, “Evidence File,” is a .wav
file containing the audio evidence sample. The circuit simulator, LTSpice will be used to filter
the audio sample with the 60 Hz bandpass filter shown in Figure 2. This filter removes the
speech and most of the noise from the sample, leaving only the 60 Hz hum from the fluorescent
lighting. It is tempting to think that any discontinuity could easily be seen on a plot of the
bandpass filtered output. But there are two problems:
1) The amplitude of the hum will change with time even though the phase may not. You
need to differentiate between amplitude and phase changes.
2) The bandpass filter is narrowband and will therefore smooth out any phase discontinuity
thereby making it difficult to see without additional processing.
To unmistakably assess the phase continuity, the phase of the incoming signal must be
compared with a known reference signal with the exact same frequency. In other words, the
phase of the incoming hum must be compared to the phase of a perfect sinewave. This is shown
on the right of Figure 1 and done using a MATLAB script.
Figure 1 – Signal Processing
The upper input to the multiplier block, !!!!!!(“”), comes from the “Filtered Evidence” signal and
contains the 60 Hz hum:
Where #!n is the amplitude of the hum, and ∅!n is its phase offset.
The second signal entering the multiplier block is the reference signal and is:
The multiplier block output is !!n(“) · !$ef(“). Using the trigonometric identity for multiplying
sinewaves ( ) allows the product to be expressed as:
If %!n = %$ef = % then this expression becomes:
The first term in this expression will have frequency equal to 2% or 120 Hz and is removed by the
lowpass filter following the multiplier leaving an output of:
It is known that #!n will exhibit fluctuations with time. Also, ∅!n would have a step change if there
was a phase discontinuity. This time dependence is captured in Equation 6.
Cos 60 Hz
44 kHz 16-bit
44 kHz 16-bit
s in ( t )
sr ef ( t )
Equation 6 shows that fluctuations in Ain will cause fluctuations in the output signal. If the signal
has a phase discontinuity, the *os(∅!n) term will change abruptly, and the result will be evident in
Summary of Steps and Grading Rubric
Figure 2 – Analog bandpass filter
This filter is an N=4 Butterworth bandpass with 3 dB frequencies of 55.0 and 65.45 Hz. There
are lots of active filter design websites (including on the Analog Devices website). If you want
more of a challenge, design this filter as an active filter and use your op-amp filter instead of this
Do the following to use the wave file Evidence File.wav, as a circuit input:
1) Make sure your wavefile is in the same directory as the schematic file.
2) Instantiate a voltage source at the circuit input.
3) Note that the voltage source will have a reference designator and a value. Value is below
the reference designator.
4) Right click on the value field. You will see a selection box where you can change the
value. (if you get a selection box with more parameters, you clicked on the voltage source
– not its value field
5) In the value field, enter the text: “wavefile=Evidence File.wav” (no quotes)
Do the following to write the circuit output to a wave file, Output.wav.
1) Right click the output node and give it the label “Out”. (no quotes)
2) Include the Spice Directive “.wave output.wav 16 44.1K Out Out” on the schematic.
Please configure your simulation as shown below:
Figure 4 – Simulation configuration for transient response
Figure 5 shows the low pass filter in the MATLAB block. This is a Finite Impulse Response (FIR)
averaging filter. It consists of exactly 3675 register locations. Input data is shifted left to right
through the register at the sample rate of 44.1 kHz. The output sample is created by summing all
the registers and then scaling by 1/3675.
Figure 5 – Lowpass filter
Close examination of Figure 5 shows that it is a discrete convolver. Since the sample rate is high
relative to the signal bandwidth, we will analyze this filter by treating the register locations as
delays as shown in Figure 4.
Figure 6 – Lowpass filter represented as a continuous-time filter. There will be 3675 delays in your filter
With the filter represented as shown in Figure 6, its impulse response can be determined by
inspection using the delay property of Laplace transforms. It is:
and, using the delay property of Laplace transforms, the transfer function is:
1 2 3 9 …
τ =1 /F S τ =1 /F S τ =1 /F S
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