Is gold a safe haven for the CIVETS countries under extremely adverse market conditions? Some new evidence from the MF-DCCA analysis

This paper investigates the safe haven attributes of gold under extreme market conditions. Our main goal is to understand if this property still holds under exceptional times characterized by unusual high levels of uncertainty. To this end, we gathered data from 2018 to 2023 for a group of emerging markets – the CIVETS (Colombia, Indonesia, Vietnam, Egypt, Turkey and South Africa) – thus encompassing a stable and a particularly turbulent period, which was marked by two consecutive crises: the COVID-19 and the Russian-Ukrainian war. We chose these countries as they are fast growing economies, which represent important investing opportunities, and because among the emerging markets these are the least studied. To achieve our goal we employed the Multifractal Detrended Cross-Correlation Analysis (MF-DCCA). Our results showed that before the pandemic the cross-correlations between gold and the financial markets were mainly negative. However, with the onset of the crisis they became positive. This demonstrates that gold lost its popular safe haven attributes and highlights the need for investors to seek alternative investments to protect downward risk, especially under extremely turbulent scenarios.

ASYMMETRIC MULTIFRACTAL CROSS-CORRELATION DYNAMICS BETWEEN FIAT CURRENCIES AND CRYPTOCURRENCIES

This paper performs the asymmetric multifractal cross-correlation analysis to examine the COVID-19 effects on three relevant high-frequency fiat currencies, namely euro (EUR), yen (YEN) and the Great Britain pound (GBP), and two cryptocurrencies with the highest market capitalization and traded volume (Bitcoin and Ethereum) considering two periods (Pre-COVID-19 and during COVID-19). For both periods, we find that all pairs of these financial assets are characterized by overall persistent cross-correlation behavior (αxy(0) > 0.5). Moreover, COVID-19 promoted an increase in the multifractal spectrum's width, which implies an increase in the complexity for all pairs considered here. We also studied the Generalized Cross-correlation Exponent, which allows us to verify that there is no asymmetric behavior between Bitcoin and fiat currencies and between Ethereum and fiat currencies. We conclude that investing simultaneously in major fiat currencies and leading cryptocurrencies can reduce the portfolio risk, leading to improvement in the investment results.

An Analysis of Dynamic Correlations among Oil, Natural Gas and Ethanol Markets: New Evidence from the Pre- and Post-COVID-19 Crisis

After the economic shock caused by COVID-19, with relevant effects on both the supply and demand for energy assets, there was greater interest in understanding the relationships between key energy prices. In order to contribute to a deeper understanding of energy price relationships, this paper analyzes the dynamics between the weekly spot prices of oil, natural gas and benchmark ethanol in the US markets. The analysis period started on 23 June 2006 and ended on 10 June 2022. This study used the DMCA cross-correlation coefficient in a dynamic way, using sliding windows. Among the main results, it was found that: (i) in the post-pandemic period, oil and natural gas were not correlated, in both short- and long-term timescales;and (ii) ethanol was negatively associated with natural gas in the most recent post-pandemic period, especially in short-term scales. The results of the present study are potentially relevant for both market and public agents regarding investment diversification strategies and can aid public policies due to the understanding of the interrelationship between energy prices. © 2023 by the authors.

VAR, ARIMAX and ARIMA models for nowcasting unemployment rate in Ghana using Google trends

The analysis of the high volume of data spawned by web search engines on a daily basis allows scholars to scrutinize the relation between the user's search preferences and impending facts. This study can be used in a variety of economics contexts. The purpose of this study is to determine whether it is possible to anticipate the unemployment rate by examining behavior. The method uses a cross-correlation technique to combine data from Google Trends with the World Bank's unemployment rate. The Autoregressive Integrated Moving Average (ARIMA), Autoregressive Integrated Moving Average with eXogenous variables (ARIMAX) and Vector Autoregression (VAR) models for unemployment rate prediction are fit using the analyzed data. The models were assessed with the various evaluation metrics of mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), median absolute error (MedAE), and maximum error (ME). The average outcome of the various evaluation metrics proved the significant performance of the models. The ARIMA (MSE = 0.26, RMSE = 0.38, MAE = 0.30, MAPE = 7.07, MedAE = 0.25, ME = 0.77), ARIMAX (MSE = 0.22, RMSE = 0.25, MAE = 0.29, MAPE = 6.94, MedAE = 0.25, ME = 0.75), and VAR (MSE = 0.09, RMSE = 0.09, MAE = 0.20, MAPE = 4.65, MedAE = 0.20, ME = 0.42) achieved significant error margins. The outcome demonstrates that Google Trends estimators improved error reduction across the board when compared to model without them.

Computational basis of SARS-CoV 2 main protease inhibition: an insight from molecular dynamics simulation based findings.

The coronavirus disease 2019 (COVID-19) pandemic is caused by newly discovered severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). One of the striking targets amongst all the proteins in coronavirus is the main protease (Mpro), as it plays vital biological roles in replication and maturation of the virus, and hence the potential target. The aim of this study is to repurpose the Food and Drug Administration (FDA) approved molecules via computer-aided drug designing against Mpro (PDB ID: 6Y2F) of SARS CoV-2 due to its high x-ray resolution of 1.95 Å as compared to other published Mprostructures. High Through Virtual Screening (HTVS) of 2456 FDA approved drugs using structure-based docking were analyzed. Molecular Dynamics simulations were performed to check the overall structural stability (RMSD), Cα fluctuations (RMSF) and protein-ligand interactions. Further, trajectory analysis was performed to assess the binding quality by exploiting the protein-residue motion cross correlation (DCCM) and binding free energy (MM/GBSA). Tenofovir, an antiretroviral for HIV-proteases and Terlipressin, a vasoconstrictor show stable RMSD, RMSF, better MM/GBSA with good cross correlation as compared to the Apo and O6K. Moreover, the results show concurrence with Nelfinavir, Lopinavir and Ritonavir which have shown significant inhibition in in vitro studies. Therefore, we conclude that Tenofovir and Terlipresssin might also show protease inhibition but are still open to clinical validation in case of SARS-CoV 2 treatment.Communicated by Ramaswamy H. Sarma.

Effects of Interdependence and Contagion on Crude Oil and Precious Metals According to ρDCCA: A COVID-19 Case Study

The energy sector has been the main economic hub in everyone's lives and in world geopolitics. Consequently, oil, gas, electricity and energy from renewable sources (wind and solar) are traded on the stock market, and all interconnected around the world. On the other hand, a global health crisis, such as COVID-19, can produce a great economic catastrophe. In this scenario, a robust statistical analysis will be performed here with respect to the concept of interdependence and contagion effect. For this project, we chose to study the relationship between the main source of energy (crude oil, WTI and Brent) and two (Gold and Silver) precious metals (which are a safe haven for investment). Therefore, with the novelty of the application of (Formula presented.) and (Formula presented.) coefficients before and during the COVID-19 crisis (announced by the World Health Organization), the interdependence and the contagion effect were calculated. We verified that COVID-19 had no influence on contagion effect between crude oil in its indexes, WTI and Brent, since they have already shown to be highly interdependent, both before and after the World Health Organization COVID-19 decree. Likewise, COVID-19 had a significant influence on the crude oil and precious metal sectors, which was evident as we identified an increase in its interdependence, with a clearly positive contagion. These results show that COVID-19 imposed a restructuring in the relationship between energy (crude oil) and precious metals. More details will be presented throughout this article. © 2023 by the authors.

Benchmark methodological approach for the application of artificial intelligence to lung ultrasound data from COVID-19 patients: From frame to prognostic-level.

Automated ultrasound imaging assessment of the effect of CoronaVirus disease 2019 (COVID-19) on lungs has been investigated in various studies using artificial intelligence-based (AI) methods. However, an extensive analysis of state-of-the-art Convolutional Neural Network-based (CNN) models for frame-level scoring, a comparative analysis of aggregation techniques for video-level scoring, together with a thorough evaluation of the capability of these methodologies to provide a clinically valuable prognostic-level score is yet missing within the literature. In addition to that, the impact on the analysis of the posterior probability assigned by the network to the predicted frames as well as the impact of temporal downsampling of LUS data are topics not yet extensively investigated. This paper takes on these challenges by providing a benchmark analysis of methods from frame to prognostic level. For frame-level scoring, state-of-the-art deep learning models are evaluated with additional analysis of best performing model in transfer-learning settings. A novel cross-correlation based aggregation technique is proposed for video and exam-level scoring. Results showed that ResNet-18, when trained from scratch, outperformed the existing methods with an F1-Score of 0.659. The proposed aggregation method resulted in 59.51%, 63.29%, and 84.90% agreement with clinicians at the video, exam, and prognostic levels, respectively; thus, demonstrating improved performances over the state of the art. It was also found that filtering frames based on the posterior probability shows higher impact on the LUS analysis in comparison to temporal downsampling. All of these analysis were conducted over the largest standardized and clinically validated LUS dataset from COVID-19 patients.

Identification of Natural Lead Compounds against Hemagglutinin-Esterase Surface Glycoprotein in Human Coronaviruses Investigated via MD Simulation, Principal Component Analysis, Cross-Correlation, H-Bond Plot and MMGBSA.

The pandemic outbreak of human coronavirus is a global health concern that affects people of all ages and genders, but there is currently still no effective, approved and potential drug against human coronavirus, as many other coronavirus vaccines have serious side effects while the development of small antiviral inhibitors has gained tremendous attention. For this research, HE was used as a therapeutic target, as the spike protein displays a high binding affinity for both host ACE2 and viral HE glycoprotein. Molecular docking, pharmacophore modelling and virtual screening of 38,000 natural compounds were employed to find out the best natural inhibitor against human coronaviruses with more efficiency and fewer side effects and further evaluated via MD simulation, PCA, DCCR and MMGBSA. The lead compound 'Calceolarioside B' was identified on the basis of pharmacophoric features which depict favorable binding (ΔGbind -37.6799 kcal/mol) with the HE(5N11) receptor that describes positive correlation movements in active site residues with better stability, a robust H-bond network, compactness and reliable ADMET properties. The Fraxinus sieboldiana Blume plant containing the Calceolarioside B compound could be used as a potential inhibitor that shows a higher efficacy and potency with fewer side effects. This research work will aid investigators in the testing and identification of chemicals that are effective and useful against human coronavirus.

Asymmetric Multifractal Analysis of the Chinese Energy Futures and Energy Stock Markets under the Impact of COVID-19

The volatility and cross-correlations of the energy market and the stock market during the COVID-19 pandemic have been paid close attention by scholars and investors. In this paper, we use the asymmetric multifractal analysis methods to study the fluctuation characteristics, market risks and cross-correlations of the Chinese energy futures market (EFM) and two energy stock markets before and after the COVID-19 outbreak, while the return series of Shanghai fuel oil futures, CSI Energy Index and CSI Mainland New Energy Theme Index are considered. The empirical evidences indicate that the auto- and cross-correlations of the three markets have the asymmetric multifractality, and that the multifractality of the cross-correlations is mainly caused by the fat-tailed distribution of the original series. After the COVID-19 outbreak, the risks of both the traditional energy stock market in the uptrend and the entire new energy stock market become larger, while those of the entire EFM become smaller. In addition, the COVID-19 pandemic has increased the multifractality of the cross-correlations between the energy futures and energy stock markets when the EFM is in downward trend.

Barter exchange as the way to deal with excess inventory: newsvendor problem with multiplicative demand

Barter exchange has been growing in popularity during the coronavirus pandemic. This article considers bartering introduced to the newsvendor model with multiplicative demand. The objective of the model is to specify the order quantity and retail price to maximize the expected profit. We distinguish cases with the co-movement of prices of exchanged products and without it. In the first case, we calculate a precise optimal solution to the problem. In the latter case, we prove the existence of an optimal solution and give the conditions under which it is unique. We examine the sensitivity analysis of the results which is illustrated in numerical examples. The analysis revealed that the greater the commission, the lower the optimal profit. We make a conclusion that barter exchange can help the retailer to improve the profit.

Automatic Scoring of COVID-19 LUS Videos Using Cross-correlation-Based Features Aggregated from Frame-Level Confidence Levels Obtained by a Pre-trained Deep Neural Network

The emergence of COVID-19 has encouraged researchers to seek a method to detect and monitor patients infected with SARS-CoV 2. The use of lung ultrasound (LUS) in this setting is rapidly spreading because of its portability, cost-effectiveness, real-time imaging, and safety. LUS has demonstrated the potential to be widely used to assess the condition of the lungs in COVID-19 patients. Given frame-level labels provided by a pre-trained deep neural network (DNN), our goal is to identify an aggregation strategy that allows to move from frame-level to video-level, which is the output required by physicians for clinical evaluation. To achieve this goal, we propose a novel aggregation method based on the cross-correlation coefficients. The logic behind this idea is that, based on the similarity between the score's variables (at frame level), the cross-correlation should be informative as to how to discriminate at video level. We applied our approach to the LUS data from a multi-center study comprising of 283, 231, and 448 LUS videos from Lodi General, Gemelli, and San Matteo Hospital, respectively. Results show that the video-level agreement with clinical experts is obtained in 87.6% of the cases, which represents a promising video-level accuracy. © 2022 IEEE.

Surveillance of SARS-CoV-2 variants of concern (VOC) in hospital wastewater (WW) and its correlation with hospitalized cases of COVID-19 and the occurrence of outbreaks

Background. WW surveillance enables real time monitoring of SARS-CoV-2 burden in defined sewer catchment areas. Here, we assessed the occurrence of total, Delta and Omicron SARS-CoV-2 RNA in sewage from three tertiary-care hospitals in Calgary, Canada. Methods. Nucleic acid was extracted from hospital (H) WW using the 4S-silica column method. H-1 and H-2 were assessed via a single autosampler whereas H-3 required three separate monitoring devices (a-c). SARS-CoV-2 RNA was quantified using two RT-qPCR approaches targeting the nucleocapsid gene;N1 and N200 assays, and the R203K/G204R and R203M mutations. Assays were positive if Cq< 40. Cross-correlation function analyses (CCF) was performed to determine the timelagged relationships betweenWWsignal and clinical cases. SARS-CoV-2 RNA abundance was compared to total hospitalized cases, nosocomial-acquired cases, and outbreaks. Statistical analyses were conducted using R. Results. Ninety-six percent (188/196) of WW samples collected between Aug/ 21-Jan/22 were positive for SARS-CoV-2. Omicron rapidly supplanted Delta by mid-December and this correlated with lack of Delta-associated H-transmissions during a period of frequent outbreaks. The CCF analysis showed a positive autocorrelation between the RNA concentration and total cases, where the most dominant cross correlations occurred between -3 and 0 lags (weeks) (Cross-correlation values: 0.75, 0.579, 0.608, 0.528 and 0.746 for H-1, H-2, H-3a, H-3b and H-3c;respectively). VOC-specific assessments showed this positive association only to hold true for Omicron across all hospitals (cross-correlation occurred at lags -2 and 0, CFF value range between 0.648 -0.984). We observed a significant difference in median copies/ ml SARS-CoV-2 N-1 between outbreak-free periods vs outbreaks for H-1 (46 [IQR: 11-150] vs 742 [IQR: 162-1176], P< 0.0001), H-2 (24 [IQR: 6-167] vs 214 [IQR: 57-560], P=0.009) and H-3c (2.32 [IQR: 0-19] vs 129 [IQR: 14-274], P=0.001). Conclusion. WWsurveillance is a powerful tool for early detection andmonitoring of circulating SARS-CoV-2VOCs.Total SARS-CoV-2 andVOC-specificWWsignal correlated with hospitalized prevalent cases of COVID-19 and outbreak occurrence.

Evaluating the connectedness of commodity future markets via the cross-correlation network

Financial markets are widely believed to be complex systems where interdependencies exist among individual entities in the system enabling the risk spillover effect. The detrended cross-correlation analysis (DCCA) has found wide applications in examining the comovement of fluctuations among financial time series. However, to what extent can such cross-correlation represent the spillover effect is still unknown. This article constructs the DCCA network of commodity future markets and explores its proximity to the volatility spillover network. Results show a moderate agreement between the two networks. Centrality measures applied to the DCCA networks are able to identify key commodity futures that are transmitting or receiving risk spillovers. The evolution of the DCCA network reveals a significant change in the network structure during the COVID-19 pandemic in comparison to that of the pre- and post-pandemic periods. The pandemic made the commodity future markets more interconnected leading to a shorter diameter for the network. The intensified connections happen mostly between commodities from different categories. Accordingly, cross-category risk spillovers are more likely to happen during the pandemic. The analysis enriches the applications of the DCCA approach and provides useful insights into understanding the risk dynamics in commodity future markets.

Pharmacophore model-aided virtual screening combined with comparative molecular docking and molecular dynamics for identification of marine natural products as SARS-CoV-2 papain-like protease inhibitors.

Targeting SARS-CoV-2 papain-like protease using inhibitors is a suitable approach for inhibition of virus replication and dysregulation of host anti-viral immunity. Engaging all five binding sites far from the catalytic site of PLpro is essential for developing a potent inhibitor. We developed and validated a structure-based pharmacophore model with 9 features of a potent PLpro inhibitor. The pharmacophore model-aided virtual screening of the comprehensive marine natural product database predicted 66 initial hits. This hit library was downsized by filtration through a molecular weight filter of ≤ 500 g/mol. The 50 resultant hits were screened by comparative molecular docking using AutoDock and AutoDock Vina. Comparative molecular docking enables benchmarking docking and relieves the disparities in the search and scoring functions of docking engines. Both docking engines retrieved 3 same compounds at different positions in the top 1 % rank, hence consensus scoring was applied, through which CMNPD28766, aspergillipeptide F emerged as the best PLpro inhibitor. Aspergillipeptide F topped the 50-hit library with a pharmacophore-fit score of 75.916. Favorable binding interactions were predicted between aspergillipeptide F and PLpro similar to the native ligand XR8-24. Aspergillipeptide F was able to engage all the 5 binding sites including the newly discovered BL2 groove, site V. Molecular dynamics for quantification of Cα-atom movements of PLpro after ligand binding indicated that it exhibits highly correlated domain movements contributing to the low free energy of binding and a stable conformation. Thus, aspergillipeptide F is a promising candidate for pharmaceutical and clinical development as a potent SARS-CoV-2 PLpro inhibitor.

Development of a deep learning-based quantitative structure-activity relationship model to identify potential inhibitors against the 3C-like protease of SARS-CoV-2.

Background: In the recent COVID-19 pandemic, SARS-CoV-2 infection spread worldwide. The 3C-like protease (3CLpro) is a promising drug target for SARS-CoV-2. Results: We constructed a deep learning-based convolutional neural network-quantitative structure-activity relationship (CNN-QSAR) model and deployed it on various databases to predict the biological activity of 3CLpro inhibitors. Subsequently, molecular docking analysis, molecular dynamics simulations and binding free energy calculations were performed to validate the predicted inhibitory activity against 3CLpro of SARS-CoV-2. The model showed mean squared error = 0.114, mean absolute error = 0.24 and predicted R² = 0.84 for the test dataset. Diosmin showed good binding affinity and stability over the course of the simulations. Conclusion: The results suggest that the proposed CNN-QSAR model can be an efficient method for hit prediction and a new way to identify hit compounds against 3CLpro of SARS-CoV-2.

The Impact of Price Support Policies and Public Emergencies on Agricultural Futures Markets - A Multifractal Cross-Correlations Analysis between China and the US

This paper investigates the presence and asymmetry of cross-correlations between agricultural futures markets in China and the US as well as the impact of price support policies and public emergencies (Sino-US trade conflict and COVID-19 pandemic) on the cross-correlations by the multifractal methods. The results show that the fluctuation characteristics and conduction directions of cross-correlations are asymmetric. The price fluctuations of soybean and corn futures in China are easier to be affected by the US soybean and corn futures. We find that the cross-correlations are multifractal under different price support policies and pubic emergencies. The price support policies with greater interventions on soybean and corn prices have aggravated the complexity of cross-correlations between the two futures markets in China and the US. The soybean and corn futures in China are hardly correlated to the US futures under the dual effect of the Sino-US trade conflict and the COVID-19 pandemic. The Sino-US trade conflict strengthens the complexity of cross-correlation for soybean futures and weakens it for corn futures, while the COVID-19 pandemic enhances the complexity of cross-correlations for soybean and corn futures. In addition, the fat-tailed probability distributions in different price support policy and public emergency periods have a dominant influence on the multifractality of cross-correlations. © 2022 World Scientific Publishing Company.

Computational investigation of peptidomimetics as potential inhibitors of SARS-CoV-2 spike protein.

Several variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were observed since the outbreak of the global pandemic at the end of 2019. The trimeric spike glycoprotein of the SARS-CoV-2 virus is crucial for the viral access to the host cell by interacting with the human angiotensin converting enzyme 2 (ACE2). Most of the mutations take place in the receptor-binding domain (RBD) of the S1 subunit of the trimeric spike glycoprotein. In this work, we targeted both S1 and S2 subunits of the spike protein in the wild type (WT) and the Omicron variant guided by the interaction of the neutralizing monoclonal antibodies. Virtual screening of two different peptidomimetics databases, ChEMBL and ChemDiv databases, was carried out against both S1 and S2 subunits. The use of these two databases provided diversity and enhanced the chance of finding protein-protein interaction inhibitors (PPIIs). Multi-layered filtration, based on physicochemical properties and docking scores, of nearly 114,000 compounds found in the ChEMBL database and nearly 14,000 compounds in the ChemDiv database was employed. Four peptidomimetics compounds were effective against both the WT and the Omicron S1 subunit with the minimum binding free energy of -25 kcal/mol. Five peptidomimetics compounds were effective against the S2 subunit with the minimum binding free energy of -19 kcal/mol. The dynamical cross-correlation matrix insinuated that the mutations of the RBD in the Omicron variant of the SARS-CoV-2 virus altered the correlated conformational motion of the different regions of the protein.Communicated by Ramaswamy H. Sarma.

Multifractal Analysis of the Impact of COVID-19 on NASDAQ, CIOPI, and WTI Crude Oil Market

In this paper, we explore the impact of COVID-19 on auto-correlations and cross-correlations among NASDAQ stock index of the USA, China iron ore price index (CIOPI), and West Texas Intermediate Crude Oil price (WTI). To find out the effect of COVID-19 on financial markets, we divide the investigated data series into two sub-periods, i.e., pre-COVID19 period and post-COVID19 period. First, multifractal detrended fluctuation analysis (MF-DFA) of those series shows a general trend of strong multifractality after COVID-19, indicating lower market efficiency after the pandemic shock. Second, multifractal detrended cross-correlation analysis (MF-DCCA) method is employed to examine cross-correlations among NASDAQ, CIOPI, and WTI. The three cross-correlations all increase in post-COVID19. The correlation between NASDAQ and CIOPI increases the most, becoming the strongest correlation among the three cross-correlations in post-COVID19. The surrogate procedure shows that the post-COVID19 cross-correlation multifractalities are mostly due to fat-tail distribution. Third, we use multi-scale multifractal analysis (MMA) to visualize the dynamic behaviors of correlations among the series. The Hurst surfaces of the three cross-correlations have more fluctuation, both at small and large scale in post-COVID19 than that of pre-COVID19. Particularly, the Hurst surface of cross-correlation between NASDAQ and CIOPI exhibits stronger multifractality during the outbreak of COVID-19 than that in both pre-COVID19 and post-COVID19. The above investigations provide helpful insights of relevant market trends.

The impact of COVID-19 on the topological properties of the Moroccan stock market network

This paper investigates the topological evolution of the Casablanca Stock Exchange (CSE) from the perspective of the Coronavirus 2019 (COVID-19) pandemic. Crosscorrelations between the daily closing prices of the Moroccan most active shares (MADEX) index stocks from March 1, 2016 to February 18, 2022 were used to compute the minimum spanning tree (MST) maps. In addition to the whole sample, the analysis also uses three sub-periods to investigate the topological evolution before, during, and after the first year of the COVID-19 pandemic in Morocco. The findings show that, compared to other periods, the mean correlation coefficient increased remarkably through the crisis period;inversely, the mean distance decreased in the same period. The MST and its related tree length support the evidence of the star-like structure, the shrinkage of the MST in times of market turbulence, and an expansion in the recovery period. Besides, the CSE network was less clustered and homogeneous before and after the crisis than in the crisis period, where the banking sector held a key role. The degree and betweenness centrality analysis showed that Itissalat Al-Maghrib and Auto Hall were the most prominent stocks before the crisis. On the other hand, Attijariwafa Bank, Banque Populaire, and Cosumar were the leading stocks during and after the crisis. Indeed, the results of this study can be used to assist policymakers and investors in incorporating subjective judgment into the portfolio optimization problem during extreme events. © Fadwa Bouhlal, Moulay Brahim Sedra, 2022.

Statistical analysis for the pitch of mask-wearing Arabic speech

According to Fourier analysis, any periodic function can be analyzed as an infinite series of trigonometric functions (sets of sines and cosines). The kernel of decay cosine yields an extension for the previous frequency-based, sieve-type detection algorithm by giving smooth peaks for decaying amplitudes with the harmonics of the signal correlation. The sequential outline of the RAPT algorithm is: 1) Providing speech samples with their sampling rate and with a reduced sampling rate. 2) Periodically, computing normalized cross-correlation function (NCCF) of the reduced sampling rate speech signal with lags in the F0 range. 3) Indicating the locations of maximum at the 1st pass of NCCF. 4) For the vicinity of the peaks in that 1st pass, calculate the NCCF for the original sampling rate. 5) Again, finding the maximum in that NCCF. Obtaining the location and amplitude of the modified peak. 6) For each peak obtained from the NCCF (high resolution), estimate the F0 of the processed frame. 7) The hypothesis of the frame for unvoiced/voiced is advanced for each frame. 8) Finding the group of the NCCF peaks via optimization process for the unvoiced/voiced hypotheses for all the frames which have the best match with the above characteristics. 9) Using the well-known speech pitch tracking algorithm (PTA), RAPT has the following differences: - PTA computes the NCCF in the linear prediction coding (LPC).