  In this paper, we examine the parameter estimation performance of three
well-known sinusoidal models for speech and audio. The first one is the
standard Sinusoidal Model (SM), which is based on the Fast Fourier Transform
(FFT). The second is the Exponentially Damped Sinusoidal Model (EDSM) which has
been proposed in the last decade, and utilizes a subspace method for parameter
estimation, and finally the extended adaptive Quasi-Harmonic Model (eaQHM),
which has been recently proposed for AM-FM decomposition, and estimates the
signal parameters using Least Squares on a set of basis function that are
adaptive to the local characteristics of the signal. The parameter estimation
of each model is briefly described and its performance is compared to the
others in terms of signal reconstruction accuracy versus window size on a
variety of synthetic signals and versus the number of sinusoids on real
signals. The latter include highly non stationary signals, such as singing
voices and guitar solos. The advantages and disadvantages of each model are
presented via synthetic signals and then the application on real signals is
discussed. Conclusively, eaQHM outperforms EDS in medium-to-large window size
analysis, whereas EDSM yields higher reconstruction values for smaller analysis
window sizes. Thus, a future research direction appears to be the merge of
adaptivity of the eaQHM and parameter estimation robustness of the EDSM in a
new paradigm for high-quality analysis and resynthesis of general audio
signals.
