Abstract
This paper addresses the issue of correctly estimating the peaks in the speech envelope of newborn infant cry signals. This method can be applied to explore brain function at early stages of child development for timely diagnosis of neonatal disease and malfonnation. The paper focuses on the performance comparison between a spectral parametric technique and the cepstrum approach. The parametric technique is based on autoregressive models whose order is adaptively estimated by means of a new technique. The cepstrum spectral resolution was improved by the Chirp Zeta Transform. The two methods were applied both to simulated and real data.
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