Robust cepstral feature for bird sound classification

Birds are excellent environmental indicators and may indicate sustainability of the ecosystem; birds may be used to provide provisioning, regulating, and supporting services. Therefore, birdlife conservation-related researches always receive centre stage. Due to the airborne nature of birds and the dense nature of the tropical forest, bird identifications through audio may be a better solution than visual identification. The goal of this study is to find the most appropriate cepstral features that can be used to classify bird sounds more accurately. Fifteen (15) endemic Bornean bird sounds have been selected and segmented using an automated energy-based algorithm. Three (3) types of cepstral features are extracted; linear prediction cepstrum coefficients (LPCC), mel frequency cepstral coefficients (MFCC), gammatone frequency cepstral coefficients (GTCC), and used separately for classification purposes using support vector machine (SVM). Through comparison between their prediction results, it has been demonstrated that model utilising GTCC features, with 93.3% accuracy, outperforms models utilising MFCC and LPCC features. This demonstrates the robustness of GTCC for bird sounds classification. The result is significant for the advancement of bird sound classification research, which has been shown to have many applications such as in eco-tourism and wildlife management.

Deep Learning Based Predictive Compensation of Flicker, Voltage Dips, Harmonics and Interharmonics in Electric Arc Furnaces

<div> <div> <div> <p>In this research work, deep machine learning based methods together with a novel data augmentation are developed for predicting flicker, voltage dip, harmonics and interharmonics originating from highly time-varying electric arc furnace (EAF) currents and voltage. The aim with the prediction is to counteract both the response and reaction time delays of active power filters (APFs) specifically designed for electric arc furnaces (EAF). Multiple synchronous Reference frame (MSRF) analysis is used to decompose the frequency components of the EAF current and voltage waveforms into dqo components. Then using low- pass filters and prediction of the future values of these dqo components, reference signals for APFs are generated. Three different methods have been developed. In two of them, a low- pass Butterworth filter is used together with a linear FIR based prediction or long short-term memory network (LSTM) for prediction. In the third method, a deep convolutional neural network (CNN) combined with a LSTM network is used to filter and predict at the same time. For a 40 ms prediction horizon, the proposed methods provide 2.06%, 0.31%, 0.99% prediction errors of the dqo components for the Butterworth and linear prediction, Butterworth and LSTM and CNN with LSTM, respectively. The error of the predicted reconstructed waveforms of flicker, harmonics, and interharmonics resulted in 8.5%, 1.90%, and 3.2% reconstruction errors for the above-mentioned methods. Finally, a Simulink and GPU based implementation of predictive APF using Butterworth filter + LSTM and a trivial APF resulted 96% and 60% efficiency on compensation of EAF current interharmonics. </p> </div> </div> </div>

Deep Learning Based Predictive Compensation of Flicker, Voltage Dips, Harmonics and Interharmonics in Electric Arc Furnaces

<div> <div> <div> <p>In this research work, deep machine learning based methods together with a novel data augmentation are developed for predicting flicker, voltage dip, harmonics and interharmonics originating from highly time-varying electric arc furnace (EAF) currents and voltage. The aim with the prediction is to counteract both the response and reaction time delays of active power filters (APFs) specifically designed for electric arc furnaces (EAF). Multiple synchronous Reference frame (MSRF) analysis is used to decompose the frequency components of the EAF current and voltage waveforms into dqo components. Then using low- pass filters and prediction of the future values of these dqo components, reference signals for APFs are generated. Three different methods have been developed. In two of them, a low- pass Butterworth filter is used together with a linear FIR based prediction or long short-term memory network (LSTM) for prediction. In the third method, a deep convolutional neural network (CNN) combined with a LSTM network is used to filter and predict at the same time. For a 40 ms prediction horizon, the proposed methods provide 2.06%, 0.31%, 0.99% prediction errors of the dqo components for the Butterworth and linear prediction, Butterworth and LSTM and CNN with LSTM, respectively. The error of the predicted reconstructed waveforms of flicker, harmonics, and interharmonics resulted in 8.5%, 1.90%, and 3.2% reconstruction errors for the above-mentioned methods. Finally, a Simulink and GPU based implementation of predictive APF using Butterworth filter + LSTM and a trivial APF resulted 96% and 60% efficiency on compensation of EAF current interharmonics. </p> </div> </div> </div>

Dealing with spatial heterogeneity in pointwise-to-gridded- data comparisons

Most studies on validation of satellite trace gas retrievals or atmospheric chemical transport models assume that pointwise measurements, which roughly represent the element of space, should compare well with satellite (model) pixels (grid box). This assumption implies that the field of interest must possess a high degree of spatial homogeneity within the pixels (grid box), which may not hold true for species with short atmospheric lifetimes or in the proximity of plumes. Results of this assumption often lead to a perception of a nonphysical discrepancy between data, resulting from different spatial scales, potentially making the comparisons prone to overinterpretation. Semivariogram is a mathematical expression of spatial variability in discrete data. Modeling the semivariogram behavior permits carrying out spatial optimal linear prediction of a random process field using kriging. Kriging can extract the spatial information (variance) pertaining to a specific scale, which in turn translates pointwise data to a gridded space with quantified uncertainty such that a grid-to-grid comparison can be made. Here, using both theoretical and real-world experiments, we demonstrate that this classical geostatistical approach can be well adapted to solving problems in evaluating model-predicted or satellite-derived atmospheric trace gases. This study suggests that satellite validation procedures using the present method must take kriging variance and satellite spatial response functions into account. We present the comparison of Ozone Monitoring Instrument (OMI) tropospheric NO2 columns against 11 Pandora spectrometer instrument (PSI) systems during the DISCOVER-AQ campaign over Houston. The least-squares fit to the paired data shows a low slope (OMI=0.76×PSI+1.18×1015 molecules cm−2, r2=0.66), which is indicative of varying biases in OMI. This perceived slope, induced by the problem of spatial scale, disappears in the comparison of the convolved kriged PSI and OMI (0.96×PSI+0.66×1015 molecules cm−2, r2=0.72), illustrating that OMI possibly has a constant systematic bias over the area. To avoid gross errors in comparisons made between gridded data vs. pointwise measurements, we argue that the concept of semivariogram (or spatial autocorrelation) should be taken into consideration, particularly if the field exhibits a strong degree of spatial heterogeneity at the scale of satellite and/or model footprints.

Spectral-Warping Based Noise-Robust Enhanced Children ASR System

In real-life applications, noise originating from different sound sources modifies the characteristics of an input signal which affects the development of an enhanced ASR system. This contamination degrades the quality and comprehension of speech variables while impacting the performance of human-machine communication systems. This paper aims to minimise noise challenges by using a robust feature extraction methodology through introduction of an optimised filtering technique. Initially, the evaluations for enhancing input signals are constructed by using state transformation matrix and minimising a mean square error based upon the linear time variance techniques of Kalman and Adaptive Wiener Filtering. Consequently, Mel-frequency cepstral coefficients (MFCC), Linear Predictive Cepstral Coefficient (LPCC), RelAtive SpecTrAl-Perceptual Linear Prediction (RASTA-PLP) and Gammatone Frequency cepstral coefficient (GFCC) based feature extraction methods have been synthesised with their comparable efficiency in order to derive the adequate characteristics of a signal. It also handle the large-scale training complexities lies among the training and testing dataset. Consequently, the acoustic mismatch and linguistic complexity of large-scale variations lies within small set of speakers have been handle by utilising the Vocal Tract Length Normalization (VTLN) based warping of the test utterances. Furthermore, the spectral warping approach has been used by time reversing the samples inside a frame and passing them into the filter network corresponding to each frame. Finally, the overall Relative Improvement (RI) of 16.13% on 5-way perturbed spectral warped based noise augmented dataset through Wiener Filtering in comparison to other systems respectively.

Methodological approaches to morbidity forecasting in military educational organizations

The article discusses aspects of the application of extrapolation and factor approaches to epidemiological forecasting, outlines the limitations and features of their application in relation to the prediction of morbidity.It is shown that when using an extrapolation approach, it becomes possible to predict the most likely numerical characteristics of morbidity in a certain time perspective. At the same time, the accuracy of the obtained forecast depends on the length of the time series and the type of long-term dynamics of morbidity. In turn, the trends formed by the results of forecasting artificially level the critical levels of morbidity that characterize individual periods of time and are fundamentally important for understanding the real picture.The factor approach is based on the prediction of morbidity levels using a certain set of factors. The difficulty of using the factor approach is noted due to the stochasticity of the epidemic process.Based on the results of a retrospective epidemiological analysis of the personalized morbidity of cadets of the Military Medical Academy, the heterogeneity of military contingents in susceptibility to acute respiratory infections of the upper respiratory tract is shown.From the standpoint of the academician V.D .Belyakov’s et al. theory of the parasitic systems self-regulation, the conclusion is made about the expediency of using a factor approach for epidemiological forecasting of morbidity in organized collectives. It is proposed to use the state of individual resistance as one of the main factors determining the epidemic well-being of organized collectives.The results of the development and testing of an electronic database that allows epidemiological surveillance of the morbidity of trainees and its linear prediction are presented.

Estimation of noise radiance point sources multichannel direction finding systems resolution by linear prediction method

The resolution of multichannel direction finding systems of independent noise radiance point sources is estimated quantitatively under a limited learning sample on the basis of a linear prediction methods “bank” and modified Capon algorithms.

Indirect impact of the COVID-19 pandemic on emergency department presentations and hospital admissions for urgent early pregnancy conditions: a population-based retrospective cohort study

Objective: To compare emergency department (ED) presentations and hospital admissions for urgent early pregnancy conditions in Victoria before and after the onset of COVID-19 lockdown on 31 March 2020. Design: Population-based retrospective cohort study Setting: Australian state of Victoria Population: Pregnant women presenting to emergency departments or admitted to hospital Methods: We obtained state-wide hospital separation data from the Victorian Emergency Minimum Dataset and the Victorian Admitted Episodes Dataset from January 1, 2018, to October 31, 2020. A linear prediction model based on the pre-COVID period was used to identify the impact of COVID restrictions. Main outcome measures: Monthly ED presentations for miscarriage and ectopic pregnancy, hospital admissions for termination of pregnancy, with subgroup analysis by region, socioeconomic status, disease acuity, hospital type. Results: There was an overall decline in monthly ED presentations and hospital admissions for early pregnancy conditions in metropolitan areas where lockdown restrictions were most stringent. Monthly ED presentations for miscarriage during the COVID period were consistently below predicted, with the nadir in April 2020 (790 observed vs 985 predicted, 95% CI 835-1135). Monthly admissions for termination of pregnancy were also below predicted throughout lockdown, with the nadir in August 2020 (893 observed vs 1116 predicted, 95% CI 905-1326). There was no increase in ED presentations for complications following abortion, ectopic or molar pregnancy during the COVID period. Conclusions: Fewer women in metropolitan Victoria utilized hospital-based care for early pregnancy conditions during the first seven months of the pandemic, without any observable increase in maternal morbidity.