Estimation and interpolation of underwater low frequency ambient noise spectrum using artificial neural networks

In this work, estimation of ambient noise spectrum influenced by wind speed and wave height carried out for the frequency range of 500 Hz to 5 kHz using Feed forward Neural Network (FNN) is presented. Ocean ambient noise measurements were made in the shallow waters of Bay of Bengal using a portable data acquisition system with a high sensitivity hydrophone at a depth of 5 m from the surface.100 sets of data covering a rage of wind speeds from 2.5 m/s to 8.5 m/s with approximately 15 sets of data falling within 1 m/s over the range of wind speed were used for training the FNN. The parameter wave height which contributes to the noise producing mechanism is also used for training along with wind speed. The results revealed that the proposed method is useful in the estimation and interpolation of underwater noise spectrum level and hence in simulation for the considered frequency range. These were confirmed by calculating the Mean Squared Error (MSE) between the experimental data and the simulation. As the measurements of the underwater ambient noise level are very difficult in remote oceanic regions, where conditions are often inhospitable, these studies seem to be relevant.