  This paper studies the classical problem of estimating the locations of
signal occurrences in a noisy measurement. Based on a multiple hypothesis
testing scheme, we design a K-sample statistical test to control the false
discovery rate (FDR). Specifically, we first convolve the noisy measurement
with a smoothing kernel, and find all local maxima. Then, we evaluate the joint
probability of K entries in the vicinity of each local maximum, derive the
corresponding p-value, and apply the Benjamini-Hochberg procedure to account
for multiplicity. We demonstrate through extensive experiments that our
proposed method, with K=2, controls the prescribed FDR while increasing the
power compared to a one-sample test.
