Technical Documentation_Embedded_Acoustic_DSP_Projects

of some
Real-Time
Embedded Acoustic
DSP Projects
April 2013

Real-Time Embedded Acoustic DSP Projects Page 2 of 17
TABLE OF CONTENTS
Page
ACRONYMS AND OTHER TERMS------------------------------------------------------------3
1.0 OBJECTIVE AND TECHNOLOGIES APPLIED ---------------------------------------4
1.1 OBJECTIVE----------------------------------------------------------------------------------------5
1.2 TECHNOLOGIES APPLIED--------------------------------------------------------------------5
2.0 THEORY AND SYSTEM FUNCTIONS----------------------------------------------------8
2.1 BUILDING BLOCKS AND FILTER DESIGN ----------------------------------------------9
2.1.1 Single Echo Effect (FIR Comb Filter) --------------------------------------------------9
2.1.2 Multiple Echo Effect (IIR Comb Filter) ------------------------------------------------9
2.1.3 All-Pass Filter ------------------------------------------------------------------------------9
2.1.4 Notch IIR Filter-----------------------------------------------------------------------------10
2.1.5 Flanger Effect-------------------------------------------------------------------------------10
2.1.6 Chorus Effect -------------------------------------------------------------------------------11
2.1.7 Phasing Effect ------------------------------------------------------------------------------11
2.1.8 Reverb Effect -------------------------------------------------------------------------------12
2.1.9 Tremelo Effect------------------------------------------------------------------------------12
2.1.10 Ring Modulation Sound Effect ---------------------------------------------------------12
2.1.11 Fuzz Effect --------------------------------------------------------------------------------13
3.0 REAL-TIME EMBEDDED DSP APPLICATION----------------------------------------14
3.1 REAL-TIME DSP ---------------------------------------------------------------------------------15
3.2 TMS320C6713 DSP ARCHITECTURE-------------------------------------------------------15
3.3 EMBEDDED SOFTWARE DEVELOPMENT CONSIDERATIONS--------------------16
3.4 SAMPLE CODE SNAPSHOT ------------------------------------------------------------------17
3.5 REFERENCES-------------------------------------------------------------------------------------17

ACRONYMS AND OTHER TERMS
1 ADC Analog-to-Digital-Converter
2 CPLD Complex Programmable Logic Device
3 CPU Central Processing Unit
4 DAC Digital-to-Analog-Converter
5 DIP Dual-Inline-Package
6 DMA Direct Memory Access
7 DSK DSP Starter Kit
8 DSP Digital Signal Processor/Processing
9 EDMA Enhanced Direct Memory Access
10 FFT Fast Fourier Transform
11 FIR Finite-length Impulse Response
12 IDE Integrated Development Environment
13 IIR Infinite-length Impulse Response
14 ISR Interrupt Service Routine
15 JTAG Joint Test Action Group
16 MIC Microphone
17 PC Personal Computer
18 RISC Reduced Instruction Set Computer
19 SDRAM Synchronous Dynamic Random Access Memory
20 USB Universal Serial Bus
21 VLIW Very Long Instruction Word

Section 1.0
OBJECTIVE AND TECHNOLOGIES APPLIED

1.1 OBJECTIVE
Audio special effects are pleasant sound variations that are created with fairly simple DSP
algorithms. Some of these effects are: echo, chorus, “flanger,” phasing, “reverb,” “tremelo,”
frequency translation, ring modulation, fuzz.
When DSP was first being taught in engineering schools, some of these audio special effects
algorism were quickly developed for musicians. However, the cost of acquiring the hardware
remained high for many years. But in the 1990s, digital and embedded electronics
implementation of the special effects quickly replaced analog implementations. Today, a
single DSP-based effects “box” is able to produce many variations of audio special effects
with improved signal-to-noise ratio.
While demonstrations using MATLAB
are extremely valuable they typically use
previously stored data/signal files and, therefore, cannot be considered “real-time”
applications. Some MATLAB
programs, using a PC sound card or data acquisition card,
have a fairly limited ability to do some real-time processing using the general purpose CPU
of the PC.
The limitations of general purpose microprocessors is as a result of the fact that performance
demands and power constraints of real-time systems often mandate specialized hardware.
This may include specialized microprocessors optimized for signal processing (digital signal
processors or DSPs), programmable logic devices, application specific integrated circuits
(ASICs), or a combination of any or all of them as required to meet system constraints. This
project aims to reinforce MATLAB
simulations by emphasizing implementations on a real-
time, embedded systems DSP platform.
Lastly, it must be emphasized that due to scarcity of resources (particularly memory) on
embedded DSP platforms (characterized by high volumes of “number crunching”), codes
must be optimized until they meet real-time constraints. Optimization is desirable as it results
in codes running faster; however, optimization tends to turn a simple straightforward DSP
algorithm in to a fairly complex one. For instance, consider the Haar Wavelet Transform
Algorithm (available on demand) I developed during the course of implementing embedded
DSP projects. Ordinarily, this implementation should require the allocation of a scratch array
of the same size as the array frame that stores the audio samples. However, the transform (in
the aforementioned algorithm) was implemented in-place without the use of any scratch
array.
1.2 TECHNOLOGIES APPLIED
1.2.1 Hardware
1) MP3 player (a cellular phone was used)
2) 2 X 3Watts pair of speakers.
3) TMS320C6713 DSK featuring:
a) A TMS320C6713 DSP operating at 225 MHz.
b) An AIC23 stereo codec with Line In, Line Out, MIC, and headphone stereo jacks.

c) 16 Mbytes of synchronous DRAM.
d) 512 Kbytes of non-volatile Flash memory (256 Kbytes usable in default
configuration).
e) 4 user accessible LEDs and DIP switches.
f) Software board configuration through registers implemented in CPLD.
g) Configurable boot options.
h) Expansion connectors for daughter cards.
i) JTAG emulation through on-board JTAG emulator with USB host interface or
external emulator.
1.2.2 Software compiler
Code Composer Studio version 5.2.1.00018 and MATLAB
version 7.7.0.471 IDEs
Figure 1.1: DSK connected to audio source and speakers

Figure 1.2: TMS320C6713 DSK Block Diagram
Figure 1.3: Workbench

Section 2.0
THEORY AND SYSTEM FUNCTIONS

2.1 BUILDING BLOCKS AND FILTER DESIGN
The building blocks for the audio special effects are the comb, all-pass and notch filters.
Other special audio effects can be constructed by appropriately cascading any of these in
series and/or parallel.
1) Single Echo Effect (FIR Comb Filter)
Y(z) = (1 + αZ
-R
).X(z) or H(z) = 1 + αZ
-R
Therefore, y(n) = x(n) + αx(n-R)
In the block diagram, there are only feed-forward paths,
thus only a single echo results. The values of the sampling
frequency and the R will determine the length of the
delay. The volume of the echo can be increased by an
increased value of α. The delay time is always constant.
2) Multiple Echo Effect (IIR Comb Filter)
Y(z).(1 - αZ
-R
) = X(z) or H(z) = 1/(1 - αZ
-R
) , |α| < 1
Therefore, y(n) = x(n) + αy(n-R)
Using a feedback path to achieve multiple echoes implies
an IIR filter. The echoes actually “repeat forever,” but the
volume of the delayed sound decreases each sample time
because of the stability condition: |α| < 1. The delay time
is always constant.
3) All-Pass Filter
Y(z).(1 + αZ
-R
) = X(z).( α + Z
-R
) or H(z) = ( α + Z
-R
) /(1 + αZ
-R
) ,
|α| < 1 is the conditions for stability.
Therefore, y(n) = -αy(n-R) + αx(n) + x(n-R)
An all-pass filter has a frequency response with a constant
magnitude. It uses a combination of feed-forward and
feedback paths with complementary gain values. It is
useful for group delay equalization to compensate for
nonlinearities. When cascaded in series with other
systems, a change in phase can be achieved while
maintaining the magnitude of the frequency response.
Z
-R
α
x(n)
n)
y(n)
Z
-R
α
y(n)x(n)
n)
y(n)
-α
α
x(n)
n)
Z
-R

Z
-ß(n)
α
x(n)
n)
y(n)
4) Notch IIR Filter
Y(z).[1 – β(1 + α)Z
-1
+ αZ
-2
]= X(z).[ (1 + α)/2].[1 – 2βZ
-1
+ Z
-2
]
or H(z) =
[ (1 + α)/2].[( 1 – 2βZ
-1
+ Z
-2
)/( 1 – β(1 + α)Z
-1
+ αZ
-2
)],
0 < α < 1, and -1 < β < 1 are the conditions for stability.
Therefore,
y(n) = -αy(n-2) + β(1 + α)y(n-2) + (1 + α)x(n) /2 - β(1 + α)x(n-1)
+ (1 + α)x(n-2) /2
A notch filter is similar to the comb filter; however, it has a single
stop band unlike the latter which has multiple, evenly-spaced
stop bands. The sharpness (width) of the stop band and location
of the stop band (notch frequency) can both be adjusted by
varying the values of α and β respectively. To set a particular
frequency fNotch in the allowable range 0 to fs (the sampling
frequency), set β = cos(2π fNotch / fs). Furthermore, the closer α
approaches 1.0, the narrower the notch width will be.
While the building blocks were presented with constant delays R and constant notch
frequency determined by β, in other to derive some other special effect, these parameters may
be varied over time. Furthermore, while some effects need just a filter stage, others might
require multiple filter stages.
5) Flanger Effect
The block diagram of the flanging effect is similar to
that of the single echo. As in the latter, α is the gain
on one of the feed forward paths. However, instead of
using a constant R for delay, β(n), which represents a
periodically varying delay, is used to vary the delay
sinusoidally. β(n) = [R/2].[1 - cos(2π fon/fs)], where fo is a
low frequency value usually lower than 1Hz and fs is the
sampling frequency. In order to produce a different
sound, β(n) can also be a periodic sawtooth or trangle
signal.
y(n)x(n)
n)
Β(1+α)
-α
-2β
(1+α)/2
1
Z
-1
Z
-1

6) Chorus Effect
The block diagram for the chorus effect is arranged
to make one musician sound like four musicians
playing the same notes. Here, three separate chorus
signals (identical to flanging except having longer
delay times) are summed with the original sound.
7) Phasing Effect
The phasing effect can be achieved in various ways.
One method uses the output of an all-pass filter
(with a depth) that is added back to the original
signal. The depth is a slowly changing delay with a
gain, just like a flanger. Due to the phase shift, some
frequencies will cancel out (while others will
reinforce) thereby creating notches, and thus the
special effect.
Another approach that is easier to fine-tune uses a
notch filter with a slowly varying notch frequency (as
the depth) that is added back to the original signal.
The two block diagrams are shown.
x(n)
n)
y(n)
α2
Z
-ß2(n)
α3
Z
-ß3(n)
α1
Z
-ß1(n)
allpass
x(n)
n)
y(n)
depth
notch
x(n)
n)
y(n)
depth

8) Reverb Effect
Reverb effect is caused
by a multitude of
reflections coming from
the walls of a concert
room. In a larger room,
one can hear these
reflections arrive at
different times.
Therefore, to
realistically simulate the
sound effect, multiple
delay effects would have
to be used. One
possible block diagram is
shown in which four IIR
comb filters (arranged in
parallel) are cascaded in
series with two all-pass
filters (arranged in
series).
Other sound effects such as tremolo, ring modulation, fuzz, compression/expansion are
created by altering the amplitude, rather than the phase, of the signal. In general, an
intentional variation in the amplitude of a signal is called Amplitude Modulation (AM). AM
is a modulation techniques used in radio communications as well as audio special effects.
9) Tremelo Effect
Tremelo is the repetitive up/down variations in the
volume of a signal. In the block diagram, the rate of the
variation in volume is determined by β, while the amount
of depth compared with the original sound is controlled
by α (0 < α < 1). Typically, β(n) = [1/2].[ 1 - cos(2π fon/fs)],
where fo is the frequency of the variation and fs is the
sampling frequency. Tremelo effect is actually a form of
AM called “double side band large carrier” (DSB-LC).
10) Ring Modulation Sound Effect
Ring modulation is a special effect whereby the audio
signal is multiplied by some other signal, usually an
internally generated constant frequency sinusoidal signal
such as β(n) = cos(2π fon/fs). Ring modulation results in
frequency translation of the original audio signal. Ring
modulation effect is actually a form of AM called “double
side band suppressed carrier” (DSB-SC).
x(n)
n)
y(n)
α1
33
43
33
3
Z
-R1
α2
33
43
33
3
Z
-R2
α3
33
43
33
3
Z
-R3
α4
33
43
33
3
Z
-R4
-α6
α6
5
Z
-R6
-α5
α5
5
Z
-R5
β(n)
1 - α
α
x(n)
n)
y(n)
β(n)
x(n)
n)
y(n)

11) Fuzz Effect
Fuzz is an intentionally introduced distortion in the signal, typically caused by “clipping” or
limiting the amplitude variations of the signal. The easiest way to implement clipping is to
adjust the gain (cascaded in series with the audio signal) to the extent that the maximum rated
ADC voltage is exceeded. This amplitude clipping eventually results in harmonic distortion
which can be added back to the original signal.

Section 3.0
REAL-TIME EMBEDDED DSP APPLICATION

3.1 REAL-TIME DSP
DSP operations proceed with the acquisition of the sampled signal – the digital signal – that
is to be processed. If the digital signal is stored for subsequent retrieval for processing, then
this cannot be said to be real-time processing. However, if the digital samples are processed
immediately they are being acquired, this is real-time processing.
Real-time processing implies that the processing of a particular sample must occur within a
given time period or the system will not operate properly. In a hard real-time system, the
system will fail if the processing is not done in a timely manner; whereas, in a soft real-time
system, the system will tolerate some failures to meet real-time targets and still continue to
operate, but with some degradation.
3.2 TMS320C6713 DSP ARCHITECTURE
Figure 3.1: Block Diagram of the TMS320C67xx DSP
The TMS320C67xx DSP is an eight-way VLIW implementation of a RISC load-store
architecture. The CPU core contains 32 general purpose registers (A0 to A15, B0 to B15) and
eight functional units split in to two clusters as shown in Figure 3.0. The statically scheduled
VLIW architecture fetches eight instructions in parallel (a fetch packet) to simultaneously
pass to its eight function units. If a function unit is not used, then it is passed a no-operation
(NOP). Each functional unit has a primary specialization (as shown in the table below) but
most are capable of multiple operations.
The A and B registers banks both have data buses for transferring data to and from the
functional units associated with them, as well as for loading and storing operands. There are
two cross paths to permit the use of a single A-side register with a B-side functional unit, and
verse versa.

Unit Integer Operations Floating Point Operations
.L Logical Arithmetic
Arithmetic / Compare Integer / floating point conversions
.S Shifts and bit fields Compare
Logical Reciprocal
Arithmetic Reciprocal square root
Branches Absolute value
Constant generation Single / double precision conversions
.M Multiply Multiply
.D Load and store Load and store
Address calculation
Addition / subtraction
3.3 EMBEDDED SOFTWARE DEVELOPMENT CONSIDERATIONS
Real-time processing can be accomplished on a sample-by-sample basis. That is, an input
sample x(t) can be converted to digital form x(n) by the DSK’s codec and transferred to the
DSK’s CPU for whatever processing desired. The processed sample y(n) will then be
transferred to the DAC part of the codec, converted back to analog form y(t) and sent to an
output device (a speaker in this case). Processing this way (sample-by-sample) has the
advantage that the system’s latency is minimized as each sample is acted upon as soon as it
arrives. However, sample-by-sample processing has serious drawbacks.
One of the implications of real-time sample-based DSP is that all processing must be
completed within the time in-between samples. For fast sampling rates (48kHz was used in
this project), this is quite impossible. Another implication of real-time sample-based DSP is
that only one sample is available for processing at any given time: this method, obviously,
cannot be used for FFT implementations as these operations require a contiguous range of
sampled data to be available at any given time.
A second implication of real-time sample-based DSP is that the processor must respond to
each interrupt from the devices (typically codecs) that are data sources and sinks in order to
perform the required data transfers. Doing this means that the current processing is
interrupted, the state of the processor preserved, and control is transferred to the relevant ISR
which is executed. This process is called context switching. Numerous context switching
introduces additional inefficiencies such as pipeline flushes and cache misses. This overhead
represents lost processing time, and can significantly reduce the overall performance of the
DSP.
It is especially for the second reason stated above that frame-based DSP, rather than sample-
based DSP, was utilized. Frame-based DSP works in consonance with the DMA controllers.
Once the DMA controller is programmed to respond to the codec (that is sourcing and
sinking data), it will automatically perform the required transfers to and from a memory
buffer without the intervention of the processor. When a buffer has been filled up or emptied,
the DMA controller then interrupts the processor. This frees the processor from the mundane
task of repetitive data transfers, and allows its resources to be focused on the
computationally-intensive processing once a buffer of data is available.

3.4 SAMPLE CODE SNAPSHOT
Figure 3.2: Code Snapshot
The code snapshot of Figure 3.2 shows the setting up of TIMER0 for synchronization of
EDMA transfers.
3.5 REFERENCES
• The Texas Instruments®
SPRU190D: TMS320C6000 Peripherals Reference Guide.
• Monson H. Hayes, Digital Signal Processing, 2012.
• James S. Walker, A Primer on Wavelets and their Scientific Applications, 2008.
• Thad B. Welch, Cameron H. G. Wright and Michael G. Morrow, Real Time Digital
Signal Processing from MATLAB®
to C with the TMS320Cx, 2012.
• Rulph Chassaing, Digital Signal Processing with the C6713 and C6416 DSK, 2005.
• The MathWorks, Inc. MATLAB®
: The Language of Technical Computing.

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