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ECE 420- Embedded DSP LaboratoryLecture 1 โ Course Overview & Audio Processing
Thomas MoonJanuary 27, 2020
D S Pigital ignal rocessing
-signal processing done by โcomputerโ
Signalsspeech/acoustic signalsmotion signals
images videos
Signals
RF signals
seismic signals stock prices
Extract useful information from the signal
filtering
low-pass filter
high-pass filter
Signal Processing
transforming
Signal Processing
Fourier Transform (FT) Wavelet Transform
modulation & demodulation
Signal Processing
frequency conversion
0 ๐#โ๐#
0 ๐ตโ๐ต
Baseband
Passband
โ3 +3+1โ1
โ3
+3
+1
โ1
๐ผ
๐
16 QAM signal
Digital Signal Processingsampling
t
ADC DAC
t
โข โข โข
Analog Digital Analog
Why DSP?
โข Digital circuits are less sensitive to external parameters (temperature, aging, etc).
โข Easy to adjust system characteristics (ex: changing filter response).
โข Digital signal can be stored almost indefinitely.
โข More suitable to process low-frequency signals.
Why DSP?
9
In this course:โข Implement the fundamental DSP concepts from
ECE 310.โข Learn to prototype & implement real-time DSP
systems.
Embedded Digital Signal Processing
10
How we learn:โข Work with hardware.โข Run practical experiment.
But, we will not build real-time DSP systems from scratch. ร Not a circuit course!
Embedded Digital Signal Processing
11
We will use Android Tablet!
โข CPU: 2.2 GHz ARM Cortex A15โข GPU: 192 core Keplerโข RAM: 2 GBโข Display: 8-inch 1920x1200 multi-touch Full-HD displayโข Motion sensors: 3-axis gyro, 3-axis accelerometer, 3-axis compassโข Cameras: Front 5MP HDR, Back 5MP auto-focus HDRโข Stereo speakers & microphone
NVIDIA SHIELD Tablets
โข CPU: 2.2 GHz ARM Cortex A15โข GPU: 192 core Keplerโข RAM: 2 GBโข Display: 8-inch 1920x1200 multi-touch Full-HD displayโข Motion sensors: 3-axis gyro, 3-axis accelerometer, 3-axis compassโข Cameras: Front 5MP HDR, Back 5MP auto-focus HDRโข Stereo speakers & microphone
NVIDIA SHIELD Tablets
YOU are responsible for the tablets.Please, use it carefully!
Software Tools You Need
PyCharm & Jupyterโข run python script for simulation.
Android Studioโข Upload algorithms into
Android platform.
1. Develop & Test DSP algorithms in high-level languages (Python or MATLAB)
โข More rapid developmentโข Test/training signals available โข Provide a reference against embedded
implementation
2. Port tested algorithms into embedded platform
Basic Practice in Developing DSP Software
15
prelab + lab (1/2)
lab (2/2)
โข Webpage: https://courses.engr.illinois.edu/ece420/sp2020
โข Lectures: Mondays 2:00-2:50PM, 4070 ECEBโข Learn theory/concept for the associated labโข Come to lecture and ask questions
โข Labs: 5072 ECEBโข ABA: 2:00-3:50 Tueโข ABC: 2:00-3:50 Wedโข ABD: 2:00-3:50 Thuโข ABE: 2:00-3:50 Fri
โข TAsโข Dimitrios Gotsisโข Spencer Markowitz
Course Info
16
โข Prerequisite: ECE 310(We assume you know basic DSP concepts!)
Course Timeline
17
Backgroundfor Labs
Special Topics/ Guest Lectures
Final Quiz
Structured Labs
Assigned Project
Final Project Demo
Final Project
Lecture Labst = 0
t = End of Semester
โข First half: 7 Structured Labs
โข Embedded DSP development frameworkโข High-level (Python) ร Embedded (Android with Java/C)
โข Different signal modalities and interfaces: IMU, audio, visual
โข Basic DSP algorithmsโข Digital filtering (lab2)โข Spectral analysis (lab3)โข Auto-correlation analysis: pitch detection/correction (lab4,5)โข Image and multidimensional signal processing (lab6,7)
Course Overview
18
โข First half: 7 Structured Labs - Format
โข Prelab [Individual]โข Complete individually prior to lab, submit to TA
โข Quiz [Individual]โข Overview of concepts from previous lecture & lab
โข Demo [Group]โข Demonstrate work from previous week to TA, answer questions
โข Lab work [Group]
Please refer to โSubmission Instructionโ page for details.
Course Overview
โข Second half: โStudent Choiceโ Group Projects (subj. to approval)
โข Start with an Assigned Project Labโข Explore implementation of a DSP algorithm from the literatureโข In Python, 2-week durationโข Jumping off point for the Final Project
โข Final projectโข Proposal and Design Reviewโข Deliverable, 2 weekly milestones, validation and test planโข Final Project Demo and Presentationโข Final Report (and optional Video)
โข Recommended not to wait until week 7 to start exploring options
Course Overview
20
โข Gradingโข Structured Labs โ 40%โข Assigned Project Lab โ 15%โข Final Project โ 40%โข Final Quiz โ 5%
โข We use Gradescope for grading. If you are not invited, please contact us.
โข Office hourโข TA Dimitrios Gotsis: Thursday 1-2pm, Friday 11am-2pm.โข Piazza
Course Overview
21
โข Goal: 1. Sample audio signals,2. Perform digital-filtering (notch filter),3. Generate a filtered sound.
Lab2 โ Audio Filtering
22
Sampling Digital Filtering+
Sampling
23
Ts
ADC
๐ฅ- ๐ก ๐ฅ[๐]๐ฅ ๐ = ๐ฅ-(๐๐5)
How/when can we reconstruct ๐ฅ- ๐กperfectly from ๐ฅ[๐]?
๐๐ก
Ts
DAC
๐ฅ[๐]
๐
๐ฅ- ๐ก๐ก
24
๐- ๐ฮฉ = :;<
<๐ฅ- ๐ก ๐;>?@๐๐ก
0 ๐ตโ๐ต
๐- ๐ฮฉ
ฮฉ = 2๐๐
To avoid the overlap, ๐ต < EFG
(or ๐ < HIFG
)
Nyquist rate : ๐นK =HFG> 2๐
CTFT
๐ ๐๐ =N;<
<
๐ฅ ๐ ๐;>OP
0 ๐ต๐5โ๐ต๐5
๐ ๐๐
๐2๐โ2๐
๐
1. Scale by Ts
2. Periodic by 2๐
โข โข โขโข โข โข
DTFT
Audio Signal
25
0๐ต = 2๐ Q 20๐๐ป๐ง
โ๐ต
๐- ๐ฮฉ
ฮฉ = 2๐๐
Highest frequency audible by humans
Hence, the sampling rate we need is HFG> 2 Q 20๐๐ป๐ง = 40๐๐ป๐ง
We will use 48kHz sampling rate.
๐- ๐ฮฉ = :;<
<๐ฅ- ๐ก ๐;>?@๐๐ก
0 ๐ตโ๐ต
๐- ๐ฮฉ
ฮฉ = 2๐๐
CTFT
โ๐ต๐5 ๐2๐โ2๐
๐ ๐๐ =N;<
<
๐ฅ ๐ ๐;>OP
0 ๐ต๐5
๐ ๐๐๐
โข โข โขโข โข โข
DTFT
0
๐[๐]
๐[๐] = NPVW
X;H
๐ฅ ๐ ๐;>IEYP/X
DFT
๐ โ 1
CTFT vs DTFT vs DFT
0 ๐ตโ๐ต
๐- ๐ฮฉ
ฮฉ = 2๐๐
0 ๐ต๐5โ๐ต๐5
๐ ๐๐
๐2๐โ2๐
๐
โข โข โขโข โข โข
CTFT
DTFT
0
๐[๐] DFT
๐ โ 1
Relation
3rd DFT bin
๐?
๐?
Given ๐5, ๐
0 ๐ตโ๐ต
๐- ๐ฮฉ
ฮฉ = 2๐๐
0 ๐ต๐5โ๐ต๐5
๐ ๐๐
๐2๐โ2๐
๐
โข โข โขโข โข โข
CTFT
DTFT
0
๐[๐] DFT
๐ โ 1
Relation
k?
๐?
10.3๐๐ป๐ง
Given ๐5, ๐
0 ๐ตโ๐ต
๐- ๐ฮฉ
ฮฉ = 2๐๐
0 ๐ต๐5โ๐ต๐5
๐ ๐๐
๐2๐โ2๐
๐
โข โข โขโข โข โข
CTFT
DTFT
0
๐[๐] DFT
๐ โ 1
Relation
2๐๐๐ = ๐
๐๐น5=
๐2๐
๐2๐
=๐๐
๐๐น5=
๐2๐
=๐๐
Digital Filter
30
+
+
+
๐ฅ[๐] ๐ฆ[๐]
๐;H
๐;H
๐;H
๐;H
๐W
๐H
๐I
โ๐H
โ๐I
โข โข โข
โข โข โข
๐ฆ ๐ = ๐W๐ฅ ๐ + ๐H ๐ โ 1 +โฏ+ ๐c๐ฅ ๐ โ ๐พ โ (๐H๐ฆ ๐ โ 1 +โฏ+ ๐e๐ฆ ๐ โ ๐ฟ )
โข L = 0, FIRโข L > 0, IIR
Examples of Filters
31
Low-pass High-pass
Band-stop Band-pass
Ramp
โข Large N (filter order)โบ Close to desired responseโบ Sharper transitionโบ Less ripplesโน More
computation/memoryโน Longer delay
Filter Design Spec
32
โข FIR vs IIRโบ Stableโบ Linear phaseโน Worse magnitude
response with same order
โข In lab2, we will implement the convolution using a circular buffer and process sample-by-sample.
Convolution
33
๐ฆ ๐ = NYV;<
<
โ ๐ ๐ฅ[๐ โ ๐]
๐ฆ ๐ = NYVW
c
โ ๐ ๐ฅ[๐ โ ๐] FIR filter (causal)
Recommended