Underwater acoustic voice communications using digital techniques

247 Pages·2017·6.98 MB·English

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Underwater acoustic voice
communications using
digital techniques
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• A Doctoral Thesis. Submitted in partial fulfilment of the requirements
for the award of Doctor of Philosophy of Loughborough University.
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Publisher: (cid:13)c Hayri Sari
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UNDERWATER ACOUSTIC VOICE COMMUNICATIONS
USING DIGITAL TECHNIQUES
by
Hayri Sari, BSc, MSc
A doctoral thesis submitted in partial fulfillment of the requirements for the award of
Doctor ofP hilosophy of the Loughborough University
April 1997
Supervisor: Professor Bryan Woodward, PhD, DIC, CEng, FlEE, FlOA, FRGS
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Department of Electronic ar... id Elettri" ca'" iJ"<',' :n: gineering
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© by Hayri Sari, 1997

To my parents to whom I dedicate this thesis
Abstract
An underwater acoustic voice communications system can provide a vital communication
link between divers and surface supervisors. There are numerous situations in which a
communication system is essential. In the event of an emergency, a diver's life may depend
on fast and effective action at the surface. The design and implementation of a digital
underwater acoustic voice communication system using a digital signal processor (DSP) is
described. The use of a DSP enables the adoption of computationally complex speech
signal processing algorithms and the transmission and reception of digital data through an
underwater acoustic channel. The system is capable of operating in both transmitting and
receiving modes by using a mode selection scheme. During the transmission mode, by
using linear predictive coding (LPC), the speech signal is compressed whilst transmitting
the compressed data in digital pulse position modulation (DPPM) format at a transmission
rate of 2400 bps. At the receiver, a maximum energy detection technique is employed to
identify the pulse position, enabling correct data decoding which in turn allows the speech
signal to be reconstructed.
The advantage of the system is to introduce advances in digital technology to underwater
acoustic voice communications and update the present analogue systems employing AM
and SSB modulation. Since the DSP-based system is designed in modular sections, the
hardware and software can be modified if the performance of the system is inadequate. The
communication system was tested successfully in a large indoor tank to simulate the effect
of a short and very shallow underwater channel with severe multipath reverberation.
The other objective of this study was to improve the quality of the transmitted speech
signal. When the system is used by SCUBA divers, the speech signal is produced in a mask
with a high pressure air environment, and bubble and breathing noise affect the speech
clarity. Breathing noise is cancelled by implementing a combination of zero crossing rate
and energy detection. In order to cancel bubble noise spectral subtraction and adaptive
noise cancelling algorithms were simulated; the latter was found to be superior and was
adopted for the current system.
1
ACKNOWLEDGMENTS
I would like to express my gratitude to my supervisor Professor Bryan Woodward for his
motivation and guidance, and for always being available as my diving test subject for
speech recordings. I also offer my appreciation for his encouragement while I was learning
to dive.
I am grateful to the Turkish Government for providing a scholarship; without its support, I
would not have been able to study in the UK.
I would like to thank Professor Zheng Zhaoning (visiting scholar) of Southeast University,
Nanjing, China for his valuable comments throughout the first year of this study. I would
like to thank to Dr. Rambod Naimimohasses for sharing his valuable experience in writing
this thesis, Dr. Robert Istepanian of the University of Portsmouth, Dr. Baochun Hou of the
University of Hertfordshire and especially to Dr. Ourania Vrondou of the Loughborough
University for their motivation. There are many other people whose friendship has kept me
going throughout my years at Loughborough University. Special thanks are due to my
colleagues Andrew Ng, Paul Lepper, Paul Connelly, Chris Richards, Hamid Reza-Alikhani
and Yiliang Song.
Finally, I would like to thank to all my family, above all my mother and father for their
love and support throughout many years.
11
List of Figures and Tables
Figure 2.1 Underwater communications 6
Figure 2.2 Illustration of several diver communication systems 14
Figure 3.1 Comparison of speech coding techniques 16
Figure 3.2 A schematic diagram of the human speech production mechanism 19
Figure 3.3 Block diagram of the simplified source filter model of speech production 20
Figure 3.4 Prediction error interpretation of the inverse all-pole filter 22
Figure 3.5 An illustration of pitch period of clear and bubble noise added speech
signals and variation of the pitch period 26
Figure 3.6 Linear predictive speech coder 29
Figure 3.7 Distributions of reflection coefficients for a sequence of speech signal 33
Figure 3.8 Measurement of excitation gain estimated from clear and noisy speech
signal and their distributions 35
Figure 3.9 Linear predictive coding synthesizer 37
Figure 3.10 Synthesis lattice structure 39
Figure 3.11 Analysed and synthesised speech signal 40
Figure 4.1 Formant frequency in air and in heliox mixture 45
Figure 4.2 The shift in average centre frequencies of Fl and F2 for four vowels, for
the phonemes of lil, lad and la! and lul, as a function of wearing a mask 47
Figure 4.3 Illustration of divers' masks used during underwater speech processing 48
Figure 4.4 Speech waveforms (first part is without mask, second part with mask)
recorded on the surface, together with their spectrogram 51
Figure 4.5 Response of a diver mask on speech signals 53
Figure 4.6 Speech waveforms recorded underwater with different diving masks,
together with their spectrogram 55
Figure 4.7 Breathing noises and spectrograms from different divers' masks 61
Figure 4.8 Amplitude distributions of breathing noise signals from divers' masks 63
Figure 4.9 Distributions of energy and zero crossing measurements for breathing
noise signals 65
Figure 4.10 Breathing noise cancellation process 67
Figure 4.11 Illustration of breathing noise cancellation 69
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