Acoustic Impulsive Noise Based on Non-Gaussian Models: An Experimental Evaluation

Abstract

In general, acoustic channels are not Gaussian distributed neither are second-order stationary. Considering them for signal processing methods designed for Gaussian assumptions is inadequate, consequently yielding in poor performance of such methods. This paper presents an analysis for audio signal corrupted by impulsive noise using non-Gaussian models. Audio samples are compared to the Gaussian, α -stable and Gaussian mixture models, evaluating the fitting by graphical and numerical methods. We discuss fitting properties as the window length and the overlap, finally concluding that the α -stable model has the best fit for all tested scenarios.

Robust time delay estimation based on non-Gaussian impulsive acoustic channel

“…According to our previous work [1], the noise characterization is presented as an impulsive noise in outdoor and indoor scenarios using fitting for SαS distributions. The outdoor environment experiences a noise with severe impulsiveness (α = 1.3), and the indoor environment with less impulsiveness (α = 1.9).…”

“…Thus, research conception of such technologies must consider realistic acoustic channel models. Notably, the channels subject to impulsive noise are more accurately characterized as non-Gaussian processes [1], [2], although many TDE methods assume implicitly or explicitly that the signal observations are Gaussian distributed. This assumption is found in generalized crosscorrelation (GCC) techniques, and their different frequency domain weighting such as phase transform (GCC-PHAT) [3], Roth weighting (GCC-ROTH), and smoothed coherence factor weighting (GCC-SCOT).…”

“…A non-Gaussian channel could be modeled with a symmetric α-stable (SαS) distribution. The SαS has heavier tails than the Gaussian distribution, giving a much better approximation to real-world audio signals [1], [2]. Although an approach based on fractional lower order statistics (FLOS) available in α-stable noises is proposed [4], this method relies on the estimating of the characteristic exponent of the model.…”

Estudo Teórico Da Relação Dos Orbitais De Fronteira Com a Eficiência De Inibição De Corrosão De Compostos Modelo De Derivados Da 2- Aminopirazina.

“…Neste trabalho, buscando distinguir as fontes de ruído gaussiano das fontes impulsivas, optou-se pelo uso de duas gaussianas ( = 2), com média = 0 e matriz de covariância Σ . Essa modelagem já foi evidenciada como apropriada para representar o ruído impulsivo em alguns experimentos práticos (Zeng et al 2013, Kozick & Sadler 2000, Pena et al 2019 1 = √ 1 2 ⋅ ( (0, 1 ) + 1 ⋅ (0, 1 )); e 2 = √ 2 2 ⋅ ( (0, 2 ) + 1 ⋅ (0, 2 )), em que 1 2 é a potência do ruído impulsivo, calculado por meio da Equação (9), e 2 2 a potência do ruído AWGN.…”

Design of a Test for Detecting the Presence of Impulsive Noise

“…Since the performance of conventional communication systems is severely degraded in such an environment, detecting the presence of impulsive noise is an important task in most communication systems [ 5 ]. Upon the detection of impulsive noise, a receiver can execute a special filter, such as a non-linearity pre-processor [ 6 , 7 ] which is designed based on the characteristics of impulsive noise, to circumvent the impulsiveness of the noise and further achieve the optimal performance in an impulsive noise environment [ 8 , 9 ].…”