  A common technique for detection of gravitational-wave signals is searching
for excess power in frequency-time maps of gravitational-wave detector data. In
the event of a detection, model selection and parameter estimation will be
performed in order to explore the properties of the source. In this paper, we
develop a Bayesian statistical method for extracting model-dependent parameters
from observed gravitational-wave signals in frequency-time maps. We demonstrate
the method by recovering the parameters of model gravitational-wave signals
added to simulated advanced LIGO noise. We also characterize the performance of
the method and discuss prospects for future work.
