  A generic `chirp' of the form h(t) = A(t) cos(phi(t)) can be closely
approximated by a connected set of multiscale chirplets with
quadratically-evolving phase. The problem of finding the best approximation to
a given signal using chirplets can be reduced to that of finding the path of
minimum cost in a weighted, directed graph, and can be solved in polynomial
time via dynamic programming. For a signal embedded in noise we apply
constraints on the path length to obtain a statistic for detection of chirping
signals in coloured noise. In this paper we present some results from using
this test to detect binary black hole coalescences in simulated LIGO noise.
