  We propose a chirp-like signal model as an alternative to a chirp model and a
generalisation of the sinusoidal model, which is a fundamental model in the
statistical signal processing literature. It is observed that the proposed
model can be arbitrarily close to the chirp model. The propounded model is
similar to a chirp model in the sense that here also the frequency changes
linearly with time. However, the parameter estimation of a chirp-like model is
simpler compared to a chirp model. In this paper, we consider the least squares
and the sequential least squares estimation procedures and study the asymptotic
properties of these proposed estimators. These asymptotic results are
corroborated through simulation studies and analysis of four speech signal data
sets have been performed to see the effectiveness of the proposed model, and
the results are quite encouraging.
