  The analysis of the time ordered data of Dark Matter experiments is becoming
more and more challenging with the increase of sensitivity in the ongoing and
forthcoming projects. Combined with the well-known level of background events,
this leads to a rather high level of pile-up in the data. Ionization,
scintillation as well as bolometric signals present common features in their
acquisition timeline: low frequency baselines, random gaussian noise, parasitic
noise and signal characterized by well-defined peaks. In particular, in the
case of long-lasting signals such as bolometric ones, the pile-up of events may
lead to an inaccurate reconstruction of the physical signal (misidentification
as well as fake events). We present a general method to detect and extract
signals in noisy data with a high pile-up rate and qe show that events from few
keV to hundreds of keV can be reconstructed in time ordered data presenting a
high pile-up rate. This method is based on an iterative detection and fitting
procedure combined with prior wavelet-based denoising of the data and baseline
subtraction. {We have tested this method on simulated data of the MACHe3
prototype experiment and shown that the iterative fitting procedure allows us
to recover the lowest energy events, of the order of a few keV, in the presence
of background signals from a few to hundreds of keV. Finally we applied this
method to the recent MACHe3 data to successfully measure the spectrum of
conversion electrons from Co57 source and also the spectrum of the background
cosmic muons.
