  We present a time-domain method to detect and correct spectral alterations of
signals by employing statistical characterization of waveforms and a
pattern-recognition procedure using simple Artificial Neural Networks. The
proposed strategy implements very-fast routines with a computational cost
proportional to the number of signal samples, being convenient for applications
in embedded environments with limited computational capabilities or fast
real-time control tasks. We use the proposed algorithms to correct spectral
amplitude attenuations in a pulse-like waveform with a sinc profile as an
application example.
