Underwater acoustic voice communications using digital techniques
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.