This document describes an experiment comparing different types of delta modulation techniques: delta modulation, delta-sigma modulation, and adaptive delta modulation. The experiment used TIMS modules to implement analog-to-digital conversion circuits for each technique. Delta modulation showed quantization noise in modulated signals and degraded speech quality after demodulation. Delta-sigma modulation reduced noise through feedback. Adaptive delta modulation further improved quality by varying the step size for different signal slopes. The document presents circuit diagrams and oscilloscope results, finding adaptive delta modulation produced the cleanest signals and most intelligible demodulated speech.

1. Abstract—Delta modulation is an analog to digital signal
conversion method in which signal quality is not of primary
importance. This experiment will replicate delta modulation,
delta-sigma modulation, adaptive delta modulation, and delta
demodulation. All components used will be TIMS modules,
namely an AUDIO OSCILLATOR, DELTA MODULATION
UTILITIES, and an ADDER.
Index Terms—delta, modulation, analog-to-digital,
demodulation
I.INTRODUCTION
ELTA modulation is an analog-to-digital and digital-to-
analog signal conversion technique used for
transmission of voice information where quality is not of
primary importance. DM is the simplest form of differential
pulse-code modulation (DPCM) where the difference between
successive samples are encoded into n-bit data streams. In
delta modulation, the transmitted data is reduced to a 1-bit
data stream. Each segment of the approximated signal is
compared to the original analog wave to determine the
increase or decrease in relative amplitude. To achieve a high
signal-to-noise ratio, delta modulation must use oversampling
techniques, that is, the analog signal is sampled at a rate
several times higher than the Nyquist rate.
D
In delta-sigma modulation, the accuracy of the modulation
is improved by passing the digital output through a 1-bit
DAC and adding (sigma) the resulting analog signal to the
input signal, thereby reducing the error introduced by the
delta-modulation. Delta-sigma modulation is used in modern
electronics such as converters, switched-mode power supplies,
and motor controllers.
With the delta modulator there is a conflict when
determining the step size. A large step size is required when
sampling those parts of the input waveform of steep slope.
But a large step size worsens the granularity of the sampled
signal when the waveform being sampled is changing slowly.
A small step size is preferred in regions where the message
has a small slope. A controllable step size can be
implemented using adaptive delta modulation, also called
adaptive delta pulse code modulation, or ADPCM. ADPCM
techniques are used in Voice over IP communications.
ADPCM was also used by Interactive Multimedia Association
for development of legacy audio codec known as ADPCM
DVI, IMA ADPCM or DVI4, in the early 1990s. ADPCM
encodes the difference between a predicted sample and the
speech sample, and provides a more efficient compression
and a reduction in the number of bits per sample, yet
preserves the overall quality of a speech sample.

Manuscript received November 15th
, 2015. This work was supported in part
by Auburn University teaching assistant Muralidharan Venkatasubramanian, as
part of ELEC 3060 for Wireless Engineers.
J. M. Ruppert and E.R. Seay are seniors studying Wireless Engineering at
Auburn University, Auburn, AL 36832.
II.PROCEDURE
A. Delta Encoding
We constructed the circuit shown in Figure 1 to implement
delta modulation. To decode, we constructed a sample and
hold, integrated, and filtered the signal through a low pass
filter. A delta modulated signal is shown in Figure 2, and a
delta modulated voice message is shown in Figure 3.
Adjusting the CLK to higher frequencies increases the
number of samples, giving a better waveform.
B. Delta-sigma Encoding
To improve the quality of the signal, we constructed the
circuit shown in Figure 4. This simulates delta-sigma
modulation. The delta-sigma modulator places an integrator
in between the source and the summer of the basic delta
modulator, and Figure 5 shows the resulting waveform, and
Figure 6 shows delta-sigma modulation and demodulation of
a voice signal.
C. Adaptive Delta Encoding
We next constructed the circuit shown in Figure 7 to
model adaptive delta modulation. The modulated signal is
shown in Figure 8, and a demodulated voice signal is
shown in Figure 9.
D. Figures
Figure 1. Delta modulation circuit
Figure 2. Delta modulated signal (green)
Delta, Delta-sigma, Adaptive delta modulation
and demodulation (December 2015)
Jeremy M. Ruppert, Edward B. Seay
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2. Figure 3. Demodulated delta signal (input speech message
in yellow, demodulated output in green)
Figure 4. Delta-sigma modulation
Figure 5. Delta-sigma modulated signal
Figure 6. Delta-sigma demodulated speech signal
Figure 7. Adaptive delta modulation
Figure 8. Adaptive delta modulated signal
Figure 9. Adaptive delta demodulated speech signal
III. EXPLANATION OF RESULTS
The delta modulation shown in Figure 2 clearly shows the
different slopes between each sampled point. These
differences are the key to understanding why the quality
improves with other delta modulation techniques. The
sawtooth waveform contains information at the message
frequency, plus unwanted components from quantizing noise,
which results in a poor SNR. Figure 3 shows the input voice
signal in yellow, and the delta modulated and then
demodulated signal in green. The resulting noise can be
easily observed, and this noise was noticeable as static when
played through a speaker, though speech could be understood.
Figure 5 shows a delta-sigma modulated signal, which has
a noticeably cleaner curve. The effects of the cleaner curve
can be seen in Figure 6 between a voice signal and the
demodulated adaptive-delta output. This output contains less
quantizing noise, and therefore a cleaner audio output, than
the delta modulation we performed prior.
Figure 7 shows the adaptive modulated signal, and it has
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3. the cleanest waveform of any thus far. Figure 8 shows an
input voice signal versus the demodulated output of adaptive
delta encoding, and Figure 9 shows adaptive delta
modulation applied to a speech signal. Adaptive delta
produced the most intelligible output.
IV. ERROR / DIFFICULTY
The most immediate challenge we came across was that the
TIMS modules we had did not include a DELTA
DEMODULATION UTILITIES, so we made one from a
sample and hold, integrator, and a low pass filter. The rest of
the procedure was very straightforward, and we did not have
any other significant areas of difficulty.
V. CONCLUSION
The differences between the various types of delta modulation
were visualized on the oscilloscope and successfully applied
to voice signals. Each type of modulation shown has its
benefits and drawbacks as well as particular uses. The key
difference between the three different methods of delta
modulation is how they alter the step size. This is most
evident when shown on a simple sine wave. By visually
comparing the noise generated by each type of modulation
and noting the audible improvement in quality, one is able to
discern with a reasonably high level of certainty between the
three modulation methods.
REFERENCES
[1] Ni.com, 'Benefits of Delta-Sigma Analog-to-Digital Conversion - National
Instruments', 2015. [Online]. Available: http://www.ni.com/white-
paper/11342/en/. [Accessed: 01- Dec- 2015].
[2] B. Baker, How delta-sigma ADCs work, Part 1, 1st ed. Texas Instruments
Incorporated, 2011.
[3] A. Loloee, 'Understanding Delta-Sigma Modulators',
Electronicdesign.com, 2013. [Online]. Available:
http://electronicdesign.com/analog/understanding-delta-sigma-modulators.
[Accessed: 01- Dec- 2015].
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