  One of the challenges in computational acoustics is the identification of
models that can simulate and predict the physical behavior of a system
generating an acoustic signal. Whenever such models are used for commercial
applications an additional constraint is the time-to-market, making automation
of the sound design process desirable. In previous works, a computational sound
design approach has been proposed for the parameter estimation problem
involving timbre matching by deep learning, which was applied to the synthesis
of pipe organ tones. In this work we refine previous results by introducing the
former approach in a multi-stage algorithm that also adds heuristics and a
stochastic optimization method operating on objective cost functions based on
psychoacoustics. The optimization method shows to be able to refine the first
estimate given by the deep learning approach and substantially improve the
objective metrics, with the additional benefit of reducing the sound design
process time. Subjective listening tests are also conducted to gather
additional insights on the results.
