We apply tipping point analysis to a large record of ocean
acoustic data to identify the main components of the acoustic
dynamical system and study possible bifurcations and transitions
of the system. The analysis is based on a statistical physics
framework with stochastic modelling, where we represent the
observed data as a composition of deterministic and stochastic
components estimated from the data using time-series techniques.
We analyse long-term and seasonal trends, system states and acoustic
fluctuations to reconstruct a one-dimensional stochastic equation
to approximate the acoustic dynamical system. We apply potential
analysis to acoustic fluctuations and detect several changes
in the system states in the past 14 years. These are most likely
caused by climatic phenomena. We analyse trends in sound pressure
level within different frequency bands and hypothesize a possible
anthropogenic impact on the acoustic environment. The tipping
point analysis framework provides insight into the structure
of the acoustic data and helps identify its dynamic phenomena,
correctly reproducing the probability distribution and scaling
properties (power-law correlations) of the time series