A Zero-Crossing Rate Property of Power Complementary Analysis Filterbank Outputs

Abstract

We establish zero-crossing rate (ZCR) relations between the input and the subbands of a maximally decimated -channel power complementary analysis filterbank when the input is a stationary Gaussian process. The ZCR at lag is defined as the number of sign changes between the samples of a sequence and its -sample shifted version, normalized by the sequence length. We derive the relationship between the ZCR of the Gaussian process at lags that are integer multiples of and the subband ZCRs. Based on this result, we propose a robust iterative autocorrelation estimator for a signal consisting of a sum of sinusoids of fixed amplitudes and uniformly distributed random phases. Simulation results show that the performance of the proposed estimator is better than the sample autocorrelation over the SNR range of to 15 dB. Validation on a segment of a trumpet signal showed similar performance gains.

Intelligent intrusion detection for optical fiber perimeter security system based on an improved high efficiency feature extraction technique

“…The ZCR is defined as the number of sign changes within a time series signal, specifically referring to the number of times the signal crosses the zero-level reference. Based on the definition, thus the ZCR can provide information about the number of zero-crossings present in a specific time period [40]. In particular, if the number of the zero-crossings is large, it implies that the given signal is undergoing rapid changes in that time period.…”

ZCR-aided neurocomputing: A study with applications

“…In conjunction with an ordinary k-means algorithm, ZCRs allowed the identification of quasi-periodic patterns that are key for their task, attaining 96.10% accuracy with 110 music clips and 140 speech files. Reciprocally, the authors of article [19] showed a ZCR-based estimator that works better than the sample autocorrelation method to analyse stationary Gaussian processes. Their work was successfully developed on the basis of a mathematical analysis of random noise.…”