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

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2827 ◽  
Author(s):  
Danilo Pena ◽  
Carlos Lima ◽  
Matheus Dória ◽  
Luan Pena ◽  
Allan Martins ◽  
...  
Keyword(s):  
Impulsive Noise ◽  
Gaussian Mixture Models ◽  
Audio Signal ◽  
Poor Performance ◽  
Gaussian Mixture ◽  
Stable Model ◽  
Gaussian Models ◽  
Non Gaussian ◽  
Signal Processing Methods ◽  
Best Fit

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.

scholarly journals Clustering Using Student t Mixture Copulas

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Till Massing
Keyword(s):  
Gaussian Mixture Models ◽  
Optimization Methods ◽  
Gaussian Mixture ◽  
Copula Models ◽  
Multimodal Distributions ◽  
Non Gaussian ◽  
Clustering Problems ◽  
Cluster Tool ◽  
Natural Way

AbstractTewari et al. (Parametric characterization of multimodal distributions with non-Gaussian modes, pp 286–292, 2011) introduced Gaussian mixture copula models (GMCM) for clustering problems which do not assume normality of the mixture components as Gaussian mixture models (GMM) do. In this paper, we propose Student t mixture copula models (SMCM) as an extension of GMCMs. GMCMs require weak assumptions, yielding a flexible fit and a powerful cluster tool. Our SMCM extension offers, in a natural way, even more flexibility than the GMCM approach. We discuss estimation issues and compare Expectation-Maximization (EM)-based with numerical simplex optimization methods. We illustrate the SMCM as a tool for image segmentation.

scholarly journals Data Assimilation with Gaussian Mixture Models Using the Dynamically Orthogonal Field Equations. Part II: Applications

2013 ◽  
Vol 141 (6) ◽  
pp. 1761-1785 ◽  
Author(s):  
Thomas Sondergaard ◽  
Pierre F. J. Lermusiaux
Keyword(s):  
Gaussian Mixture Models ◽  
Mean Field ◽  
Information Criterion ◽  
Gaussian Mixture ◽  
Sudden Expansion ◽  
Test Case ◽  
Field Equations ◽  
Gaussian Statistics ◽  
Non Gaussian ◽  
Orthogonal Field

Abstract The properties and capabilities of the Gaussian Mixture Model–Dynamically Orthogonal filter (GMM-DO) are assessed and exemplified by applications to two dynamical systems: 1) the double well diffusion and 2) sudden expansion flows; both of which admit far-from-Gaussian statistics. The former test case, or twin experiment, validates the use of the Expectation-Maximization (EM) algorithm and Bayesian Information Criterion with GMMs in a filtering context; the latter further exemplifies its ability to efficiently handle state vectors of nontrivial dimensionality and dynamics with jets and eddies. For each test case, qualitative and quantitative comparisons are made with contemporary filters. The sensitivity to input parameters is illustrated and discussed. Properties of the filter are examined and its estimates are described, including the equation-based and adaptive prediction of the probability densities; the evolution of the mean field, stochastic subspace modes, and stochastic coefficients; the fitting of GMMs; and the efficient and analytical Bayesian updates at assimilation times and the corresponding data impacts. The advantages of respecting nonlinear dynamics and preserving non-Gaussian statistics are brought to light. For realistic test cases admitting complex distributions and with sparse or noisy measurements, the GMM-DO filter is shown to fundamentally improve the filtering skill, outperforming simpler schemes invoking the Gaussian parametric distribution.

scholarly journals Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 21 ◽  
Author(s):  
Andrej Zgank
Keyword(s):  
Markov Models ◽  
Predictive Coding ◽  
Gaussian Mixture Models ◽  
Audio Signal ◽  
Gaussian Mixture ◽  
Classification Performance ◽  
Learning Approaches ◽  
Linear Predictive Coding ◽  
Mel Frequency Cepstral Coefficients ◽  
Acoustic Classification

Beekeeping is one of the widespread and traditional fields in agriculture, where Internet of Things (IoT)-based solutions and machine learning approaches can ease and improve beehive management significantly. A particularly important activity is bee swarming. A beehive monitoring system can be applied for digital farming to alert the user via a service about the beginning of swarming, which requires a response. An IoT-based bee activity acoustic classification system is proposed in this paper. The audio data needed for acoustic training was collected from the Open Source Beehives Project. The input audio signal was converted into feature vectors, using the Mel-Frequency Cepstral Coefficients (with cepstral mean normalization) and Linear Predictive Coding. The influence of the acoustic background noise and denoising procedure was evaluated in an additional step. Different Hidden Markov Models’ and Gaussian Mixture Models’ topologies were developed for acoustic modeling, with the objective being to determine the most suitable one for the proposed IoT-based solution. The evaluation was carried out with a separate test set, in order to successfully classify sound between the normal and swarming conditions in a beehive. The evaluation results showed that good acoustic classification performance can be achieved with the proposed system.

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