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SirenDetect. Jennifer Michelstein Department of Electrical Engineering Adviser: Professor Peter Kindlmann May 1, 2003. Alerting Drivers about Emergency Vehicles. GPS. INSIDE A CAR. BUTTONS. CALENDAR/MINI PC. INSIDE A CAR. RADIO, CD PLAYER, SPEAKERS. PHONE WITH BUTTONS IN STEERING WHEEL.

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SirenDetect

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Sirendetect

SirenDetect

Jennifer Michelstein

Department of Electrical Engineering

Adviser: Professor Peter Kindlmann

May 1, 2003

Alerting Drivers about Emergency Vehicles


Inside a car

GPS

INSIDE A CAR

BUTTONS


Inside a car1

CALENDAR/MINI PC

INSIDE A CAR

RADIO, CD PLAYER, SPEAKERS

PHONE WITH BUTTONS IN STEERING WHEEL


Cars have too many distractions

Cars have too many distractions.

Added features = Less attention to the road and other vehicles.

Driving has become perilous.


Sirendetect summary of key features

  • Using microphones, collect street sounds.

SirenDetect Summary of Key Features

  • Using DSP chip, analyze incoming data. Determine if sound is a siren.

  • If yes, alert driver. If no, continue sampling.


Two part process

1) Software Solution

Matlab version 6.0 to analyze frequencyplots of sirens, construct filtering algorithms.

Two-Part Process:

2) Hardware Solution

Motorola DSP56826evm Digital Signal Processing kit with Metrowerks CodeWarrier software to create the device.


Sample siren

Sample Siren

Amplitude

Time


Fast fourier transform fft

The FFT is an algorithm that “reduces the number of computations from something on the order of N02 to N0 log N0.”*

Fast Fourier Transform (FFT)

*http://aurora.phys.utk.edu/~forrest/papers/fourier/#FFT


Fft of sample siren

FFT of Sample Siren

Peak 1:

105 – 180 Hz

Peak 3:

Peak 2:

370 - 430 Hz

220 – 300 Hz


The butterworth filter

= 1

The Butterworth Filter

N - order of the filter;  - analog frequency; s - complex Laplace variable such that s =  + j.

* Kuc, Roman. Introduction to Digital Signal Processing. New York: McGraw-Hill, 1988.


Butterworth filtering with matlab

Butterworth Filtering with Matlab

n - order of the filter

Wn - the two-element vector Wn = [f1, f2] where f1, f2 are the limits of the passband, scaled from 0 to 1

[b,a] - filtering coefficients

By experiment, n1 = n2 = 8; n3 = 6.


Matlab algorithm

1. Create three bandpass Butterworth filters for P1, P2, P3.

2. Construct filters in parallel; pass siren through filters.

3. Obtain peak amplitude of each key region.

4. Repeat steps 2-3 for multiple siren samples; average

amplitudes to obtain typical A1, A2, A3.

5. Run current sound sample through parallel filters; compare

resulting amplitudes to A1, A2, and A3 to determine if sample

is a siren.

Matlab Algorithm


Sirendetect

Filtering: Graphical Representation of Inputs


Sirendetect

Filtering: Graphical Representation of Outputs

Filter 1

Filter 2

Filter 3

Bandpass Region 1

Bandpass Region 2

Bandpass Region 3

Max(Peak1)

Max(Peak2)

Max(Peak3)


Next how to compare bandpass regions

1. Absolute Magnitude Comparisons.

Advantages: Simple(st), Fast(est), and Inexpensive Implementation.

Disadvantages: Inaccuracy in presence of noise, disturbances.

Next: How to compare bandpass regions?

2. Respective Magnitude Comparisons.

Advantages: Simple, Fast, and Inexpensive; high accuracy.

Disadvantages: Robustness? Testing necessary.


How to compare bandpass regions continued

How to compare bandpass regions? (continued)

3. Time Domain.

Advantages: High Accuracy.

Disadvantages: Difficult, more costly implementation.

4. Combination of 2 (Relative Magnitudes) and 3 (Time Domain).

Advantages: Greatest Accuracy.

Disadvantages: Costly and possibly redundant algorithm.


Sirendetect

Testing various algorithms proved that comparing respective amplitudes was the ideal implementation. Adjusting thresholds after testing various siren samples made detection more reliable.


Sirendetect

Matlab Code for Relative Amplitudes

if (x13 > ??) & (x13 < ??) true2 = 1;end;if (x23 > ??) & (x23 < ??) true3 = 1;end;if (true1) & (true2) & (true3) its_a_siren=1;end;

x12 = max1 / max2;x13 = max1 / max3;x23 = max2 / max3;true1 = 0;true2 = 0;true3 = 0;its_a_siren=0;if (x12 >??) & (x12 < ??) true1 = 1;end;


Flow chart top level

Start Engine

Key:

R1 = max(x1)/max(x2)

R2 = max(x1)/max(x3)

R3 = max(x2)/max(x3)

FFT Algorithm for

Flow Chart - Top Level

all incoming sound

LED OFF

n

y

R1?

n

y

R2?

y

n

R3?

LED ON


Block diagram

Real Time Data Stream

Block Diagram

Bandpass

Filter 1

Bandpass

Filter 2

Bandpass

Filter 3

max(A1)

max(A2)

max(A3)

A1/A2

A2/A3

A1/A3

Comparisons to R1, R2, R3

LED’s on / off


Repeat process on motorola dsp56826evm digital signal processing kit with metrowerks codewarrier

Repeat Process on Motorola DSP56826evm Digital Signal Processing kit with Metrowerks CodeWarrier.

Test in Laboratory Setting using microphones oriented in various directions; sirens mixed with street noises, music, etc.


The future of sirendetect

1. Siren Library Expansion.

SirenDetect will store a fingerprint for each of the

unique siren sounds emitted by emergency vehicles.

The Future of SirenDetect

2. Siren Differentiation.

SirenDetect will distinguish between the siren of an

ambulance, fire truck, police car, or other emergency

vehicle. The driver will be notified of the type of car

that is approaching.


Sirendetect

The Future of SirenDetect (continued)

3. Directional Capabilities.

Strategic positioning of microphones will allow

SirenDetect to compare amplitudes of signals and

identify the precise location from which the emergency

vehicle is approaching.


Sirendetect

The Future of SirenDetect (continued)

4. Robustness Evaluation.

The system must be tested in residential areas, on highways,

in cars traveling the same direction as emergency vehicles, the

opposite direction, and at various angles (30, 45, 60, 90, etc.).

Possible hazards are the Doppler effect and noise interference.

The thresholds employed in the current code may need

slight adjustments to be comprehensive in various driving

conditions, yet should not be all-inclusive (i.e. detecting

too many noises as sirens). Efficiency - Accuracy Trade-Off.


Acknowledgments

Acknowledgments

Peter Kindlmann, Project Adviser

Edward Jackson

Roman Kuc


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