Abstract:In this paper, the author proposes a new SEIRS model that generalizes several classical deterministic epidemic models (e.g., SIR and SIS and SEIR and SEIRS) involving the relationships between the susceptible S, exposed E, infected I, and recovered R individuals for understanding the proliferation of infectious diseases. As a way to incorporate the most important features of the previous models under the assumption of homogeneous mixing (mass-action principle) of the individuals in the population N, the SEIRS model utilizes vital dynamics with unequal birth and death rates, vaccinations for newborns and non-newborns, and temporary immunity. In order to determine the equilibrium points, namely the disease-free and endemic equilibrium points, and study their local stability behaviors, the SEIRS model is rescaled with the total time-varying population and analyzed according to its epidemic condition R 0 for two cases of no epidemic (R 0 ≤ 1) and epidemic (R 0 > 1) using the time-series and phase portraits of the susceptible s, exposed e, infected i, and recovered r individuals. Based on the experimental results using a set of arbitrarily-defined parameters for horizontal transmission of the infectious diseases, the proportional population of the SEIRS model consisted primarily of the recovered r (0.7-0.9) individuals and susceptible s (0.0-0.1) individuals (epidemic) and recovered r (0.9) individuals with only a small proportional population for the susceptible s (0.1) individuals (no epidemic). Overall, the initial conditions for the susceptible s, exposed e, infected i, and recovered r individuals reached the corresponding equilibrium point for local stability: no epidemic (DFE X DFE ) and epidemic (EE X EE ).

This paper introduces the Greenwood Function Cepstral Coefficient (GFCC) and Generalized Perceptual Linear Prediction (GPLP) feature extraction models for the analysis of animal vocalizations across arbitrary species. These features are generalizations of the well-known MelFrequency Cepstral Coefficient (MFCC) and Perceptual Linear Prediction (PLP) approaches, tailored to take optimal advantage of available knowledge of each species' auditory frequency range and/or audiogram data. Illustrative results are presented comparing use of the GFCC and GPLP features versus MFCC features over the same frequency ranges.

This paper presents an approach to song-type classification and speaker identification of Norwegian Ortolan Bunting (Emberiza Hortulana) vocalizations using traditional human speech processing methods. Hidden Markov Models (HMMs) are used for both tasks, with features including Mel-Frequency Cepstral Coefficients (MFCCs), log energy, and delta (velocity) and delta-delta (acceleration) coefficients. Vocalizations were tested using leave-one-out cross-validation. Classification accuracy for 5 song-types is 92.4%, dropping to 63.6% as the number and similarity of the songs increases. Song-type dependent speaker identification rates peak at 98.7%, with typical accuracies of 80-95% and a low end at 76.2% as the number of speakers increases. These experiments fit into a larger framework of research working towards methods for acoustic censusing of endangered species populations and more automated bioacoustic analysis methods.

In this paper, the authors present optimal multichannel frequency domain estimators for minimum meansquare error (MMSE) short-time spectral amplitude (STSA), log-spectral amplitude (LSA), and spectral phase estimation in a widely distributed microphone configuration. The estimators utilize Rayleigh and Gaussian statistical models for the speech prior and noise likelihood with a diffuse noise field for the surrounding environment. Based on the Signal-to-Noise Ratio (SNR) and Segmental Signal-to-Noise Ratio (SSNR) along with the Log-Likelihood Ratio (LLR) and Perceptual Evaluation of Speech Quality (PESQ) as objective metrics, the multichannel LSA estimator decreases background noise and speech distortion and increases speech quality compared to the baseline single channel STSA and LSA estimators, where the optimal multichannel spectral phase estimator serves as a significant quantity to the improvements, and demonstrates robustness due to time alignment and attenuation factor estimation. Overall, the optimal distributed microphone spectral estimators show strong results in noisy environments with application to many consumer, industrial, and military products.