This document provides an overview and instructions for generating real-time digital audio signals using filtering on an ARM FM4 processor. It introduces the Cypress FM4 hardware and development tools used, including Keil uVision and LabVIEW. It then covers generating sine waves of different frequencies on the FM4 by programming an interrupt service routine with a lookup table approach. Debugging and visualizing the generated audio signals using an oscilloscope is demonstrated. Designing and implementing finite impulse response (FIR) filters for audio processing on the FM4 is also outlined.

1. Real-Time Digital Audio
Filtering on embedded
ARM processors
(Cypress FM4)
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2. Introduction
Electronics-ICT
@ Hogeschool PXL
https://www.facebook.com/pbaeaict/
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3. Prerequirements
• Cypress FM4-176L-S6E2CC-ETH hardware
• http://www.cypress.com/documentation/development-kitsboards/sk-fm4-176l-s6e2cc-fm4-family-quick-
start-guide
• Audio jack cables (2)
• Install LabVIEW 2017 from http://www.ni.com/nl-be/shop/labview/download.html <Not the NXG
version!>
• Install Keil uvsion from https://www.keil.com/demo/eval/arm.htm <32kb limit edition is ok for
the labs>
• Cypress PDL library (2.1.0 ; not 3.0) from: http://www.cypress.com/documentation/other-
resources/peripheral-driver-library-pdl-release-notes-archive
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4. Contents
Cypress ARM FM4
20 min
Keil uvision
10 min
LabVIEW oscilloscope
and function generator
2 hours
Programming in Keil
uvision <IDE Setup>
1 hour
Generate a Sine wave
on Cypress ARM FM4
1 hour
Designing FIR filters for
Cypress ARM FM4
4 hours
Case study, remove
unwanted sine wave in
audio stream using a filter
4 hours
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5. Lab Setup
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6. Cypress FM4-176L-
S6E2CC
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7. SK-FM4-176L-S6E2CC http://www.cypress.com
https://www.arm.com/
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8. Vincent Claes

9. Cypress FM4-176L-S6E2CC-ETH Pioneer Kit
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10. SK-FM4-176L-S6E2CC
https://www.arm.com/
http://www.cypress.com
CORTEXM4
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11. S6E2CC
• 32-bit general purpose series based on ARM Cortex-M4
• 2MB flash memory
• 256 kb SRAM
• DSP and floating point (FPU) functions
• I²S port for communication with Audio codex
• ADC, DAC,…
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12. Board Block diagram
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13. Board user button and LED
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14. Audio codec
• WM8731 (U3) codec connected to I²S and I²C
• Low power stereo with integrated headphone driver
• Independently programming the ADC and DAC sample rate from a single clock
source
• Signals ADCLRC and DACLRC
• CN11 => microphone jack
• CN5 => headphone jack
• CN6 => line-in jack
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15. Keil uvsion Vincent Claes

16. Lab Setup
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17. Keil uvision
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18. ARM Software Tools and Libraries
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19. ARM Software Tools and Libraries
Cortex Microcontroller Software Interface Standard (CMSIS)
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20. LabVIEW Vincent Claes

21. Lab Setup
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22. LabVIEW
• Introduction LabVIEW programming
• Programming a Signal Generator
• Programming an Oscilloscope
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23. LabVIEW: Hello World!
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24. Vincent Claes

25. UI
CODE
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26. Front Panel
UI Building blocks
- Controls => values from UI to code
- Indicators => values from code to UI
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27. Block Diagram
Code Building blocks
- Programming structures
- Variables
- Connectivity
- …
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28. LabVIEW Example Code
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29. LabVIEW Example Code
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30. Programming in Keil
uvision
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31. Lab Setup
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32. GPIO on ARM FM4 [GPIO_1]
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33. Debugging
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34. GPIO on ARM FM4 [GPIO_2]
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35. Discrete-Time Linear Invariant System
x(n)
input signal
discrete-time
LTI system
y(n)
output signal
{time-varying} {time-varying}
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36. Classification of signals
 Continuous-time vs. discrete-time
 Periodic vs. aperiodic
 Deterministic vs. random
 Energy vs. power
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37. Continuous-Time vs. Discrete-Time
-1 0 t-2 1 2 3
0
1
u(t)
 A continuous -time signal x(t) is defined for all values of time, t
 x(t) need not be a continuous function of time, e.g. unit step
 A discrete-time signal x(n) = x(nT) is defined only at discrete values of
time t=nT.
the unit step function u(t) is an
example of a continuous-time
signal containing a discontinuity
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38. Converting from Continuous to Discrete Time
A discrete-time signal may be formed by sampling a continuous-time signal
t
0
x(t)
kT (k+1)T (k+2)T
T
x(t)
x(nT)
sampler
x(t) nT
quantiser
x(n)
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39. Periodic vs. Aperiodic Signals
A continuous -time signal x(t) is periodic if and only if
The smallest positive value of T for which this is the case is the period of the signal
A discrete-time signal x(n) is periodic if and only if
Any signal that is not periodic is aperiodic.
)()( Ttxtx 
)()( Nnxnx 
for all t.
for all n.
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40. Deterministic vs. Random Signals
 Deterministic signals are described as algebraic functions of time.
 Random (stochastic) signals are described in terms of their statistical
properties.
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41. Impulse (Delta or Dirac) Function
For continuous-time systems
For discrete-time systems
1)(
0,0)(





dtt
tt












1)(
0,0
0,1
)(
nd
n
n
nd
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42. Unit Step Function









 dttu
t
t
tu
)()(
0,0
0,1
)(








k
ndku
n
n
nu
)()(
0,0
0,1
)(
For continuous-time systems
For discrete-time systems
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43. Sinusoid Function
)sin()( ttx 
)sin()( Tnnx 
Sinusoidal signals pass through (any) linear time-invariant system with no change
to their shape.
For continuous-time systems
For discrete-time systems
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44. Complex Exponential Function
tj
Aetx 
)(
Tjn
Aenx 
)(
Rotating vector (phasor) in complex plane.
Closely related to sinusoidal signals.
Projections onto real and imaginary axes of complex plane are cosine and sine respectively.
Im
Re
ωt
Asin(ωt)
Acos(ωt)
rotating vector
For continuous-time systems
For discrete-time systems
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45. Euler’s Formula
)(
2
1
)sin( tjtj
ee
j
t 
 

 
)sin()cos( tjte tj


)(
2
1
)cos( tjtj
eet 
  
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46. Sampling and Reconstruction
Can we recreate from discrete-time samples the continuous-time
signal from which they were taken?
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47. Digital to Analogue Conversion Using a Zero-
Order Hold
T
1
0 t
t
T
t
h(t)
t
y(t) = y(nT) * h(t)
A zero-order hold (ZOH) has the following impulse response
T is the sampling period
)()( nTyny 
digital to analogue conversion using a zero-order hold
y(nT)
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48. Digital to Analogue Conversion Using a Zero-
Order Hold
T
1
0 t
t
T
t
h(t)
t
y(t) = y(nT) * h(t)
A zero-order hold (ZOH) has the following impulse response
T is the sampling period
)()( nTyny 
digital to analogue conversion using a zero-order hold
y(nT)
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49. Discrete Time Convolution
An arbitrary input signal y(n) may be decomposed into a sum of (delayed)
weighted impulses.
Corresponding output is formed by summing (delayed) weighted impulse responses.
d(n)
Delta sequence
LTI
system
y(n)
Impulse response
-1 0 n1 2 3
h(n)
-1 0 n1 2 3
d(n)
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50. Convolution
Consider convolution from the point of view of the input signal
Each weighted impulse at the system input results in a weighted impulse response
at the system output.
Each input sample contributes to a number of output sample values.
 )2()2()1()1()()0()( ndxndxndxnx
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51. Convolution
0 n1 2 3
response to x(0)
4 50 n1 2 3 4 5
0 n1 2 3
x(n)
arbitrary input
signal (sequence)
LTI
system
y(n)
impulse response
-1 0 n1 2 3
h(n)
0 n1 2 3
x(n)
0 n1 2 3
4
0 n1 2 3 4 5
4 5
output signal y(n) comprises
sum of responses
0 n1 2 3 4 50 n1 2 3 4 5
0 n1 2 3 4 5
response to x(1)
response to x(3)
response to x(2)
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52. Convolution
A more practical and useful approach is to consider convolution from the point of
view of the output signal
Each output sample value can be computed based on a number of input
sample values
If impulse response is finite
 )2()2()1()1()()0()( ndhnxhnxhny
Nnnh  0,0)(


N
k
knhkxny
0
)()()(
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53. Convolution
 Convolution is a fundamental and important building block in digital signal
processing.
 Its implementation is a sum of products.
 Single cycle MAC and Harvard architecture are suited to its efficient
computation.
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54. Properties of Convolution
Convolution involving the delta sequence is particularly straightforward
Commutative property
Associative property
Distributive property
)()(*)( nxndnx 
)()(*)( snxsndnx 
)()(*)( nKxnKdnx 
)(*)()(*)( nanbnbna 
))(*)((*)()(*))(*)(( ncnbnancnbna 
))()((*)()(*)()(*)(( ncnbnancnanbna 
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55. Correlation
 


ppmymxpR
m
xy )()()(
 Correlation is concerned with determining the degree of similarity between
two signals
 Computationally it bears a resemblance to convolution
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56. Correlation
 


ppmymxpR
m
xy )()()(
 Sum of products
 At the heart of the discrete Fourier transform (DFT)
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57. Correlation vs. Convolution
 The similarities between the computations involved in convolution and
correlation
are coincidental.
 Convolution describes the relationships between input signal, output signal
and
impulse response in a LTI system.
 Correlation is a method of determining the degree of similarity between two
signals.
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58. Digital Signal Processing System
ADC DAC
Digital
Signal
processor
Analogue
input signal
Analogue
output signal
CODEC on the audio card
Microcontroller
(ARM Cortex-M4)
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59. Aliasing – antialiasing filters
ADC DAC
sampling rate 8 kHz1 kHz
input signal output signal
sampled signal
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60. Aliasing – antialiasing filters
ADC DAC
sampling rate 8 kHz7 kHz
input signal output signal
sampled signal
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61. Aliasing – antialiasing filters
ADC DAC
sampling rate 8 kHz1 kHz
input signal output signal
sampled signal
cut off frequency 4 kHz
low pass
filtered signal
LPF
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62. Aliasing – antialiasing filters
ADC DAC
sampling rate 8 kHz
LPF
cut off frequency 4 kHz7 kHz
input signal
low pass
filtered signal
sampled signal
output signal
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63. Sine Wave
Generation
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64. Lab Setup
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65. Basic Audio I/O ARM FM4 [AUDIO_1]
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66. Basic Audio I/O ARM FM4 [AUDIO_1]
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67. Audio Delay on ARM FM4 [AUDIO_1]
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68. ARM FM4 : Audio Sine Generation [LUT-AUDIO_1]
• sine_lut_intr.c
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69. ARM FM4 : Audio Sine Generation [LUT-AUDIO_1]
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70. ARM FM4:Audio Sine Gen [LUT_BUF-AUDIO_1]
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71. ARM FM4:Audio Sine Gen [LUT_BUF-AUDIO_1]
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72. Practice
• Generate sine of 500Hz
• Generate sine of 1000Hz
• Generate sine of 2000Hz
• Generate sine of 3000Hz
• You should be able to achieve these simply by changing the initialised contents of
the array sine_table (and by changing the value of the constant LOOP_SIZE
accordingly). Do not change any other program statements. Record the
combinations of LOOP_SIZE and sine_table with which you achieve these results.
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73. Visualisation of memory contents
• Run the program and then halt it by clicking on the Stop toolbar
button. type the variable name buffer as the Address in the
debugger's Memory 1 window. Set the displayed data type to
Decimal and Float as shown in the figure below.
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74. Visualisation of memory contents
• Type the following command at the prompt in the debugger's
Command window to save the contents of array buffer to a file in
your project folder.
• SAVE <filename> <start address>, <end address>
• for example, SAVE sinusoid.dat 0x20000848, 0x200009D8
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75. Viewing the output <octave online>
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76. Audio Sine Generation on ARM FM4 [AUDIO_2]
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77. Audio Sine Generation on ARM FM4 [AUDIO_2]
• Change variable frequency value to
• 1500
• 2573
• 7000
• 3500
• 4500
• Watch the results on your oscilloscope!
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78. Audio Sine Generation on ARM FM4 [AUDIO_2]
• Change in sine_lut_intr.c :
• sine_table[LOOPLENGTH] = {10000, 10000, 10000, 10000, -10000, -
10000, -10000, -10000};
• Square wave?
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79. Audio Sine Generation on ARM FM4 [AUDIO_2]
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80. Audio Sine Generation on ARM FM4 [AUDIO_2]
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81. Moving Average Filter on ARM FM4
moving
average
filter
x(n) y(n)
input output
5
)4()3()2()1()(
)(


nxnxnxnxnx
ny
)4(2.0)3(2.0)2(2.0)1(2.0)(2.0)(  nxnxnxnxnxny
five point moving average filter




1
0
)(
1
)(
N
i
inx
N
ny
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82. Moving Average Filter on ARM FM4
0 5 10 15 20
0
1
2
3
4
5
6
7
sample number
samplevalue
input x(n) x output y(n) ●
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83. Moving Average Filter on ARM FM4
input x(n) x output y(n) ●
0 5 10 15 20
0
1
2
3
4
5
6
7
sample number
samplevalue
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84. Moving Average Filter on ARM FM4
input x(n) x output y(n) ●
0 5 10 15 20
0
1
2
3
4
5
6
7
sample number
samplevalue
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85. Moving Average Filter on ARM FM4
input x(n) x output y(n) ●
0 5 10 15 20
0
1
2
3
4
5
6
7
sample number
samplevalue
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86. Moving Average Filter on ARM FM4
input x(n) x output y(n) ●
0 5 10 15 20
0
1
2
3
4
5
6
7
sample number
samplevalue
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87. Moving Average Filter on ARM FM4 [AUDIO_3]
𝑦 𝑛 =
1
𝑁
𝑖=0
𝑁−1
𝑥(𝑛 − 𝑖
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88. Moving Average Filter on ARM FM4 𝑦 𝑛 =
1
𝑁
𝑖=0
𝑁−1
𝑥(𝑛 − 𝑖
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89. Moving Average Filter on ARM FM4 𝑦 𝑛 =
1
𝑁
𝑖=0
𝑁−1
𝑥(𝑛 − 𝑖
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90. Finite Impulse Response (FIR) Filter
N point moving average filter




1
0
)(
1
)(
N
i
inx
N
ny




1
0
)()()(
N
i
inxihny
Compare this with the conventional representation of an N point FIR filter
h(i) are referred to as the coefficients of the filter
and as the impulse response of the FIR filter
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91. Finite Impulse Response (FIR) Coefficients
The coefficients, h(i), of an N point moving average
filter each have a value of 1/N
-1 0 1 2 3 4 5 6
0
0.05
0.1
0.15
0.2
0.25
coefficient number
coefficientvalue
Graphical representation of the coefficients of a 5 point
moving average filter, also known as its impulse response
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92. Finite Impulse Response (FIR) Coefficients
z1
z1
z1
h(4)h(0) h(1)

z1
h(2) h(3)
x(n) x(n-1) x(n-2) x(n-3) x(n-4)
h(0)x(n) h(1)x(n-1) h(2)x(n-2) h(3)x(n-3) h(4)x(n-4)




1
0
)()()(
N
i
inxihny
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93. Finite Impulse Response (FIR)
z1
z1
z1
h(4)h(0) h(1)

z1
h(2) h(3)
1 0 0 0 0
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94. Finite Impulse Response (FIR)
z1
z1
z1
h(4)h(0) h(1)

z1
h(2) h(3)
1 0 0 0 0
h(0) 0 0 0 0
y(n) = h(0)
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95. Finite Impulse Response (FIR)
z1
z1
z1
h(4)h(0) h(1)

z1
h(2) h(3)
0 1 0 0 0
0 h(1) 0 0 0
y(n) = h(1)
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96. Finite Impulse Response (FIR)
z1
z1
z1
h(4)h(0) h(1)

z1
h(2) h(3)
0 0 1 0 0
0 0 h(2) 0 0
y(n) = h(2)
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97. Finite Impulse Response (FIR)
z1
z1
z1
h(4)h(0) h(1)

z1
h(2) h(3)
0 0 0 1 0
0 0 0 h(3) 0
y(n) = h(3)
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98. Finite Impulse Response (FIR)
z1
z1
z1
h(4)h(0) h(1)

z1
h(2) h(3)
0 0 0 0 1
0 0 0 0 h(4)
y(n) = h(4)
output sequence h(0) h(1) h(2) h(3) h(4)
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99. Filters
Low pass
High pass
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100. Filters
Band pass
Band stop
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101. FIR Filters Vincent Claes

102. Lab Setup
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103. ARM FM4: FIR filter implementation [AUDIO_3]
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104. ARM FM4: FIR filter implementation [AUDIO_3]
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105. ARM FM4: FIR filter implementation [AUDIO_3]
Input: 1750Hz
Input: 2500Hz
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106. ARM FM4: FIR filter implementation [AUDIO_3]
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107. ARM FM4: FIR filter implementation [AUDIO_3]
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108. ARM FM4: FIR filter implementation [AUDIO_3]
• Experiment with:
• #include "ave5.h"
• #include "lp33.h“
• #include “bp41.h”
• #include “bp55.h”
• #include “bs790.h”
• #include “bs7200.h”
• Filter X: h(n) = {0.2, 0.4, -0.4, -0.2}
• Filter Y: h(n) = {0.5, -0.5}
• Filter Z: h(n) = {0.5, 1.0, 0.5}
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109. ARM FM4: FIR filter implementation [AUDIO_3]
Efficiency update using CMSIS DSP library arm_fir_f32();
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110. ARM FM4: FIR filter implementation [AUDIO_3]
Efficiency update using CMSIS DSP library arm_fir_f32();
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111. ARM FM4: FIR filter implementation [AUDIO_3]
Efficiency update using CMSIS DSP library arm_fir_f32();
Block Processing using DMA transfers
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112. ARM FM4: FIR filter implementation [AUDIO_3]
Efficiency update using CMSIS DSP library arm_fir_f32();
Block Processing using DMA transfers
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113. ARM FM4: FIR filter implementation [AUDIO_3]
Efficiency update using CMSIS DSP library arm_fir_f32();
Block Processing using DMA transfers
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114. ARM FM4: FIR filter implementation [AUDIO_3]
Efficiency update using CMSIS DSP library arm_fir_f32();
Block Processing using DMA transfers
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115. ARM FM4: FIR filter implementation [AUDIO_3]
Efficiency update using CMSIS DSP library arm_fir_f32();
Block Processing using DMA transfers
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116. ARM FM4: FIR filter implementation [AUDIO_3]
Efficiency update using CMSIS DSP library arm_fir_f32();
Block Processing using DMA transfers
Program GPIO Measurement divided by BUFSIZE
fir_prbs_intr.c 8.25 µs N/A
fir_prbs_dma.c 1.35 ms 10.55 µs
fir_prbs_CMSIS_intr.c 5.96 µs N/A
fir_prbs_CMSIS_dma.c 183 µs 1.43 µs
- Max time available at sample rate 8kHz => 125 µs for the interrupt based programs
- Max time available between consecutive calls to function process_buffer() used in the DMA
applications is BUFSIZE/(fs) = 16 ms
Play with the filter coefficients by including a different header file, the time to compute each output sample will depend on the
number of filter coefficients used. Vincent Claes

117. DMA-base I/O on FM4 S6E2CCA
• DMA: Direct Memory Access
• Transfer data at high speed without using the CPU
• Improves system performance
• Cypress has 2 peripherals that have DMA access for transferring data
• DSTC: Descriptor System Data Transfer Controller
• Descriptor based DMA: instead of saving characteristics of each tansfer into registers
(size of transfer, source address, destination address,…) all the parameters are packed in
32bits descriptors and stored in the RAM memory. This reduces the size of the peripheral
and allows more channels (256 DSTC channels vs 8 DMAC channels).
• DMAC: Direct Memory Access Controller
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118. DMA-base I/O on FM4 S6E2CCA
• Data from and to I2S peripheral using DMA : use of DSTC
• audio_init() => one DSTC channel to make DMA transfers between
the output buffers arrays (dma_tx_buffer_ping en dma_tx_buffer_pong) and
the I2S peripheral. It generates an interrupt when a transfer of
DMA_BUFFER_SIZE 32-bit samples has completed
• Another DSTC channel is configured to make DMA transfers between the I2S
peripheral and the input buffers in memory (dma_rx_buffer_ping and
dma_rx_buffer_pong). It generates an interrupt when a transfer of
DMA_BUFFER_SIZE 32-bit samples has completed
• The same interrupt service routine (ISR) is used for both DMA processes
Vincent Claes

119. DMA-base I/O on FM4 S6E2CCA
• Actions in routine are:
• Assigning to pointers rx_proc_buffer and tx_proc_buffer the values PING and PONG.
• Switch between buffers dma_tx_buffer_ping, dma_tx_buffer_pong, dma_rx_buffer_ping and
dma_rx_buffer_pong
• Set flags rx_buffer_full and tx_buffer_empty => are used in proc_buffer()
• If rx_proc_buffer is equal to PING, DSTC1 has filled buffer dma_rx_buffer_ping, and this data
is available to process.
• If tx_proc_buffer is equal to PING, DSTC0 transfer has written the contents to buffer
dma_tx_buffer_ping to the I2S peripheral and this buffer is available to be filled with new
data.
Vincent Claes

120. DMA-base I/O on FM4 S6E2CCA
• Function main() is waiting until both rx_buffer_full and tx_buffer_empty
flags are set. This is when both DMA transfers have completed, before
calling function proc_buffer()
• In loop_dma.c function proc_buffer() simply copies the contents of the
most recently filled input buffer (dma_rx_buffer_ping or
dma_rx_buffer_ping), to the most recently emptied output buffer
(dma_tx_buffer_ping or dma_tx_buffer_ping) according to the values of
pointers rx_proc_buffer and tx_proc_buffer. In general frame-based
processing will be carried out in function proc_buffer() using the contents
of the most recently filled input buffer as input and writing output sample
values to the most recently emptied output buffer.
Vincent Claes

121. DMA-base I/O on FM4 S6E2CCA
• DMA transfers will complete, function proc_buffer() will be called,
every DMA_BUFFER_SIZE sampling instants and therefore any
processing must be completed within DMA_BUFFER_SIZE/fs seconds
(strictly speaking, before the next DMA transfer completion)
Vincent Claes

122. Case Study Vincent Claes

123. Case Study
• Filter out ,by implementing Filters on the ARM FM4, the sine signals
mixed in given MP3’s
Vincent Claes

124. More information
• https://armkeil.blob.core.windows.net/product/gs_MDK5_4_en.pdf
• http://t-filter.engineerjs.com/
• http://complextoreal.com/tutorials/
• https://www.arm.com/
• http://www.cypress.com/
• https://www.ni.com
Vincent Claes

125. My contact details
• Feel free to contact me:
• vincent[dot]claes_at_pxl[dot]be
• https://www.linkedin.com/in/vincentclaes/
• My passion: Teaching students, chips and machines new tricks.
• FPGA, Machine Learning, LabVIEW and startups
• Special thanks to ARM Ltd. and Cypress Semiconductors
Vincent Claes