CovidCoughNet: A new method based on convolutional neural networks and deep feature extraction using pitch-shifting data augmentation for covid-19 detection from cough, breath, and voice signals.

This study proposes a new deep learning-based method that demonstrates high performance in detecting Covid-19 disease from cough, breath, and voice signals. This impressive method, named CovidCoughNet, consists of a deep feature extraction network (InceptionFireNet) and a prediction network (DeepConvNet). The InceptionFireNet architecture, based on Inception and Fire modules, was designed to extract important feature maps. The DeepConvNet architecture, which is made up of convolutional neural network blocks, was developed to predict the feature vectors obtained from the InceptionFireNet architecture. The COUGHVID dataset containing cough data and the Coswara dataset containing cough, breath, and voice signals were used as the data sets. The pitch-shifting technique was used to data augmentation the signal data, which significantly contributed to improving performance. Additionally, Chroma features (CF), Root mean square energy (RMSE), Spectral centroid (SC), Spectral bandwidth (SB), Spectral rolloff (SR), Zero crossing rate (ZCR), and Mel frequency cepstral coefficients (MFCC) feature extraction techniques were used to extract important features from voice signals. Experimental studies have shown that using the pitch-shifting technique improved performance by around 3% compared to raw signals. When the proposed model was used with the COUGHVID dataset (Healthy, Covid-19, and Symptomatic), a high performance of 99.19% accuracy, 0.99 precision, 0.98 recall, 0.98 F1-Score, 97.77% specificity, and 98.44% AUC was achieved. Similarly, when the voice data in the Coswara dataset was used, higher performance was achieved compared to the cough and breath studies, with 99.63% accuracy, 100% precision, 0.99 recall, 0.99 F1-Score, 99.24% specificity, and 99.24% AUC. Moreover, when compared with current studies in the literature, the proposed model was observed to exhibit highly successful performance. The codes and details of the experimental studies can be accessed from the relevant Github page: (https://github.com/GaffariCelik/CovidCoughNet).

An acoustic analysis and comparison between remotely collected and in-person laboratory collected data in vocal imitation of pitch

Restrictions on face-to-face interactions due to the outbreak of the coronavirus pandemic (COVID-19) in early 2020 have impacted experimental behavioral research. The rapid change from in-person to online data collections has been challenging in many behavioral studies, especially those that require vocal production, and the quality of the remotely collected data needs to be investigated. The current study examines the recording quality and corresponding measures of vocal production accuracy in online and in-person settings using two measurements: harmonic-to-noise ratio (HNR) and fundamental frequency, f 0. Participants imitated pitch patterns extracted from recordings of song or speech, either in a laboratory or via an online platform. The results showed that the recordings from the online setting had higher HNR than those from the in-person setting, whereas the pitch imitation accuracy in both settings did not differ. We also report an experiment that simulated differences between the online and in-person settings within participants, focusing on software used, type of microphone, and presence of ambient noise. Pitch accuracy did not differ according to these variables, except ambient noise, whereas HNR again varied across conditions. Based on these results we conclude that measures of pitch accuracy are reliable across these different types of data collection, whereas finer-grained spectral measures like HNR might be affected by various factors. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Digitalizing a Multimodal Genre-Based Approach to Teaching Elevator Pitch: Pedagogical Implications and Students' Experiences

The health situation brought about by COVID-19 has contributed to the emergence and implementation of novel teaching formats (e.g., hybrid, online) in higher education. This scenario, characterized by the increasing use of technology and digital resources, calls for a methodological and pedagogical shift to best support the teaching and learning process. This study aims to discuss the digitalization of a multimodal genre-based approach to teaching Elevator Pitch presentations in the ESP context. In doing so, we report on the methodological adaptations needed to digitalize and implement the pedagogical proposal and the implications of its transition to an online environment. In addition, this study explores the learning experiences of students as recipients of the pedagogical proposal. Methodologically, a survey was administered to identify students' perceptions of the application of the pedagogical proposal. The results provide insights into the students' perceptions of its implementation concerning the classroom dynamics, the digital resources used, and the presentation format. The study concludes with a reflection on the implications of carrying out digitalization and the transition to online environments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

BERIS: An mBERT-based Emotion Recognition Algorithm from Indian Speech

Emotions, the building blocks of the human intellect, play a vital role in Artificial Intelligence (AI). For a robust AI-based machine, it is important that the machine understands human emotions. COVID-19 has introduced the world to no-touch intelligent systems. With an influx of users, it is critical to create devices that can communicate in a local dialect. A multilingual system is required in countries like India, which has a large population and a diverse range of languages. Given the importance of multilingual emotion recognition, this research introduces BERIS, an Indian language emotion detection system. From the Indian sound recording, BERIS estimates both acoustic and textual characteristics. To extract the textual features, we used Multilingual Bidirectional Encoder Representations from Transformers. For acoustics, BERIS computes the Mel Frequency Cepstral Coefficients and Linear Prediction coefficients, and Pitch. The features extracted are merged in a linear array. Since the dialogues are of varied lengths, the data are normalized to have arrays of equal length. Finally, we split the data into training and validated set to construct a predictive model. The model can predict emotions from the new input. On all the datasets presented, quantitative and qualitative evaluations show that the proposed algorithm outperforms state-of-the-art approaches. © 2022 Association for Computing Machinery.

Zoomvesting: angel investors' perception of subjective cues in online pitching

PurposeThe COVID-19 pandemic transformed angel investment meetings from in-person to online. The purpose of this paper is to explore whether this move affected angel investors' perception of subjective behavioral cues in pitch sessions within a large Brazilian angel group.Design/methodology/approachThis study followed an exploratory approach using a triangulation process that combined observation, documents and interviews. Data collected by observation, document studies, and interviews were themed, coded, and organized during the research.FindingsThe move from in-person to online pitches did not seem to affect levels of trustworthiness or arrogance as angels assessed more message content during Q&A sessions. Body movement, gestures and "eye gaze” (i.e. the look on a presenter's face) played a central role in passion assessment during in-person meetings. Body language was highly limited during online sessions and tone of voice became the main source of passion assessment.Research limitations/implicationsThe findings of this study suggest that pitches at online meetings affect angel investors' perception of founders' subjective cues, particularly cues pertaining to passion. Entrepreneurs should be trained to convey passion with tone of voice and to improve their body language in the context of webcam use. The interviews with volunteer sampling were subject to volunteer bias. Additionally, the findings may be affected by cultural context.Practical implicationsA practical contribution of this study is to highlight the need for entrepreneurs to be trained for online pitches. In an online setting, body language is limited, but it is still possible to use one's hands and tone of voice to connect better to investors.Originality/valueThis study is unique because it captures the transition of angel investment meetings from in-person affairs before the pandemic to online meetings during the pandemic crisis. These unique circumstances provided a real-world laboratory to observe founders' subjective cue effects on angel investment decision-making.

LASU quarantine dye: Antimicrobial Phthalocyanine Activated by Indoor Light

Novel safe and stable teracationic Zinc phthalocyanine is efficient against bacteria, fungi and viruses also under indoor light Recently a novel photosensitizer with outstanding properties, phthalocyanine LASU has being developed. The compound possesses unprecedented stability and antimicrobial activity. It can be activated by a weak indoor light of 270 lux and shows the activity against G+ and G- bacteria as well as fungi and viruses. Over 3 log inactivation of bacteria and fungi on the surface of a LASU-impregnated material can be achieved in 1/2-1 h of illumination with a regular indoor and/or natural light. A cotton filter impregnated with 0.1 g/m2 of LASU eradicates on its surface the coronavirus HCoV-229E by 3.5 log in 30 minutes under indoor/natural light of 500 lux. The molecule is photostable and remains active for weeks with no significant bleaching. Another remarkable property is its ability to bind to cellulose support. It readily attaches to a fiber substrate through electrostatic interactions, moreover, the size of LASU ring matches the pitch of cellulose polymer, making the conjugate unusually strong. Hence the compound does not leech to water and is stable against temperature and surfactants. The toxicology studies also reveals that substance is non-irritating for human skin, and is non-mutagenic, which makes it suitable for human-wearable items.Copyright © 2023

Detection of COVID-19 by Cough Sound: A Method Based on DSC + BiLSTM

In recent years, the outbreak of COVID-19 has brought a new round of challenges to global health care, and daily large-scale testing has also increased the consumption of medical resources. However, studies have shown that the cough sounds of patients with COVID-19 are significantly different from other Characteristics of respiratory infectious diseases. Therefore, this paper considers the use of the patient's cough as a detection sample to give the preliminary screening results. The research was conducted on the COUGHVID dataset. The experiment is divided into two stages: (1) Preprocessing stage: use Pitch Shift and Time Stretch to perform data enhancement on audio data, and use spec Augment to perform data enhancement on mel spectrogram. (2) Model construction stage: use two layers of DSC and one layer of BILSTM to splicing to obtain a classification model. Finally, the method is compared with the baseline method using only two layers of LSTM. The results show that accuracy has increased by 1.9%, F1 has increased by 1.9%, and AUC has increased by 1.6%. © 2022 IEEE.

Comparative Analysis of Pitch Ratings in All Formats of Cricket

The article identifies measurable attributes to find the consistency in pitch ratings which are otherwise subjective decisions made by referees for international test match cricket. To do so, the article uses statistics related to test matches, one-days and T20s played among all test playing nations between March 2017 and March 2019 (53 tests, 142 T20s and 172 one-day matches: the next two seasons ending May 2020/2021 were hit by COVID-19 and hence excluded). To measure the consistency of pitch ratings (very good, good, above average, average, below average and poor), measurable attributes like runs/day, wickets/day, runs/over, runs/wicket and overs/wicket were identified. To rank pitch ratings using these attributes, the multi-criteria decision-making technique—PROMETHEE II was used. We found that the referee pitch ratings are largely consistent and the attributes developed can be utilized to further analyse future judgements regarding pitch ratings. Further, six-pitch ratings can be clustered into two distinct groups that are significantly different from each other. The article is among the first to analyse sports pitch ratings by using team performance-based statistics. This study paves the way for similar studies and development of newer statistical flow-based attributes. © 2023 XLRI Jamshedpur, School of Business Management & Human Resources.

Study of Ct Severity Index in Covid-19 Patients in Tertiary Care Hospital of Maharashtra

Background: Covid-19 is an infection that has widely and rapidly spread all over the world and become a pandemic with significantly impacts upon the socio-political milieu and health care system. CT scan has high sensitivity in patients infected by covid-19 when other test like nasal pharyngeal swabs may be insensitive hence CT severity index has potential impact on clinical decision making for covid-19. Material(s) and Method(s): 50 patients aged between 18 to 50 years admitted at covid-19 ward were studied with CT chest scan images 120kvp automatic tube current modulation (30-70 mAS) pitch 0.99 - 1.22 mm slice thickness 10 mm, FoV=350 mmx 350 mm. All images were reconstructed with a slice interval of 0.625 to 1.250 mm opacity, (GGO), consolidation GGO with consolidation, linear opacities, and crazy paving halo sign reverse halo sign. Result(s): CT chest findings had 4 (8%) has 1 lobe affected, 6 (12%) had 2 lobes affected, 5 (10%) had 3 lobes affected, 3 (6%) had 4 lobes affected, 1 (2%) had 5 lobes affected. 4 (8%) had GGO with consolidation, 14 (28%) had GGO, 1 (2%) had consolidation. Frequency of lobe, 6 (12%) had right middle lobe, 14 (28%0 had right lower lobe, 3 (6%) had left upper lobe, 4 (8%) left lingual lobe, 15 (30%) had left lower lobe. The distribution of opacification and pattern had rounded shape, 2 (4%0 had linear opacity, 4 (8%) crazy paving pattern, 1 (2%) reverse halo sign, 4 (8%) halo sign, 13 (26%) had peripheral distribution. CT severity score was 4 (8%) severe, 32 (64%) moderate, 14 (28%) mild. Conclusion(s): CT scan study has significant role in management of severity and possible outcome of covid-19. CT severity scores can be positively correlated with inflammatory laboratory markers. Copyright © 2022 Authors.

Analysis of Learning Effect through Voice Signal Analysis in Online Education Environment

The new coronavirus infection (COVID-19) that appeared suddenly has permeated our daily lives and changed our way of life. In the field of education, education is being conducted in a non-face-to-face teaching method to prevent the spread of coronavirus. In the end, e-Learning, a new educational and training system that can provide a lifelong education environment in the 21st century information society, is increasing in use in the field of education. The biggest advantage of the online education system is that it provides an environment in which you can learn the necessary contents anytime, anywhere. However, there are cases in which the learning effect is reduced because various learning support is not available in the online space due to the sudden change of the educational environment due to the covid-19. Therefore, in this thesis, a study was conducted to analyze the learning effect of online education with voice signals for college students who are receiving education through an online education environment. To this end, the learning effect was classified into a group saying that the learning effect increased and a group that decreased due to online education, and the voices of the subjects in each group were collected. As a result of the experiment, the results of the vocal cord vibration (Pitch), Degree of voice breaks, Jitter and Shimmer were consistent among the elements of voice signal analysis between two groups. Copyright © 2022 by the author(s).

Gender in Philippine advertisements: Portrayal patterns and platform differences immediately before the COVID-19 pandemic

Informed by the pioneering work of Erving Goffman, subsequent studies by Mee-Eun Kang, recent innovations in the Geena Davis Institute, and local approaches by Philippine scholars, this study sought to determine the portrayal of gender in Philippine advertising across various media. Specifically, it located gender across products, storylines, characters, pitch, and setting. It provided the baseline data for the depiction of gender immediately before the pandemic, and served as the benchmark against how gender may be portrayed differently in ads in the next normal. The population of TV, print, and radio advertisements was based on a database maintained by Aries Insights and Media Solutions (AIMS), access to which was facilitated by Kantar Media. The study covered two TV stations, three broadsheets, two tabloids, two FM stations, and two AM stations. The researchers constructed two weeks from January - December 2018 for TV and print, while one week was constructed from October to December 2018 for radio. Across platforms, the most advertised products were food and non-alcoholic beverages, pharmaceutical/health/herbal products, and restaurants, retail outlets, and malls. Ads focused on product/brand prestige, appealed to any of the five human senses, or concentrated on health. Certain ad pitches showed noticeable gendered differences. Women rather than men were associated with beauty, youth, and value for money. Print and radio ads featured more men than women, while TV featured more women than men. The study found that while overt objectification was no longer prevalent in advertisements, stereotypically gendered portrayals remain in subtle forms across platforms. © 2022

Accent Classification of Native and Non-Native Children using Harmonic Pitch

To combat the Covid-19 outbreak, the education system shifted away from the classroom to distinct e-learning on digital platforms, which made effective use of voice-based recognition systems, especially for preliterate children. Children’s speech recognition systems face multiple challenges owing to their immature vocal tracts, and they demand more intelligence due to the fact that children with diverse accents utter words differently. Accent refers to a unique style of pronouncing a language, particularly one associated with a specific nation, place, or socio-economic background. This paper aims to extract reliable acoustic and prosodic speech cues of accent for classification of native and non-native preschool children using harmonic pitch estimation along with Mel Frequency Cepstral Coefficients (MFCCs) to train the k-Nearest Neighbour (k-NN) classifier. The experimental results reveal that the proposed robust model outperforms various feature extractors in accent classification of native and non-native children in terms of accuracy & F-Measure and more discriminate against noisy environments. © 2022 IEEE.

Harmonicity aids hearing in noise.

Hearing in noise is a core problem in audition, and a challenge for hearing-impaired listeners, yet the underlying mechanisms are poorly understood. We explored whether harmonic frequency relations, a signature property of many communication sounds, aid hearing in noise for normal hearing listeners. We measured detection thresholds in noise for tones and speech synthesized to have harmonic or inharmonic spectra. Harmonic signals were consistently easier to detect than otherwise identical inharmonic signals. Harmonicity also improved discrimination of sounds in noise. The largest benefits were observed for two-note up-down "pitch" discrimination and melodic contour discrimination, both of which could be performed equally well with harmonic and inharmonic tones in quiet, but which showed large harmonic advantages in noise. The results show that harmonicity facilitates hearing in noise, plausibly by providing a noise-robust pitch cue that aids detection and discrimination.

INSIDE OUT

This text was written in response to a commission from Berliner Festspiele/MaerzMusik – Festival for Time Issues 2021. Juliet Fraser collaborated with filmmaker Jessie Rodger to create an experimental video essay that layered raw footage of Juliet in her erstwhile London studio space, reading her own words, reflecting on the words of others and doing her singing practice. The result offered a glimpse into one artist's efforts to remain creative and connected during the time of Covid. What follows is the written essay;the full video essay, Inside Out, can be seen at www.youtube.com/watch?v=m96xf41-_PA.

Zoomvesting: angel investors' perception of subjective cues in online pitching

Purpose The COVID-19 pandemic transformed angel investment meetings from in-person to online. The purpose of this paper is to explore whether this move affected angel investors' perception of subjective behavioral cues in pitch sessions within a large Brazilian angel group. Design/methodology/approach This study followed an exploratory approach using a triangulation process that combined observation, documents and interviews. Data collected by observation, document studies, and interviews were themed, coded, and organized during the research. Findings The move from in-person to online pitches did not seem to affect levels of trustworthiness or arrogance as angels assessed more message content during Q&A sessions. Body movement, gestures and "eye gaze" (i.e. the look on a presenter's face) played a central role in passion assessment during in-person meetings. Body language was highly limited during online sessions and tone of voice became the main source of passion assessment. Research limitations/implications The findings of this study suggest that pitches at online meetings affect angel investors' perception of founders' subjective cues, particularly cues pertaining to passion. Entrepreneurs should be trained to convey passion with tone of voice and to improve their body language in the context of webcam use. The interviews with volunteer sampling were subject to volunteer bias. Additionally, the findings may be affected by cultural context. Practical implications A practical contribution of this study is to highlight the need for entrepreneurs to be trained for online pitches. In an online setting, body language is limited, but it is still possible to use one's hands and tone of voice to connect better to investors. Originality/value This study is unique because it captures the transition of angel investment meetings from in-person affairs before the pandemic to online meetings during the pandemic crisis. These unique circumstances provided a real-world laboratory to observe founders' subjective cue effects on angel investment decision-making.

Analysis of COVID-19 Resulting Cough Using Formants and Automatic Speech Recognition System.

As part of our contributions to researches on the ongoing COVID-19 pandemic worldwide, we have studied the cough changes to the infected people based on the Hidden Markov Model (HMM) speech recognition classification, formants frequency and pitch analysis. In this paper, An HMM-based cough recognition system was implemented with 5 HMM states, 8 Gaussian Mixture Distributions (GMMs) and 13 dimensions of the basic Mel-Frequency Cepstral Coefficients (MFCC) with 39 dimensions of the overall feature vector. A comparison between formants frequency and pitch extracted values is realized based on the cough of COVID-19 infected people and healthy ones to confirm our cough recognition system results. The experimental results present that the difference between the recognition rates of infected and non-infected people is 6.7%. Whereas, the formant analysis variation based on the cough of infected and non-infected people is clearly observed with F1, F3, and F4 and lower for F0 and F2.

Threat or opportunity? A case study of digital-enabled redesign of entrepreneurship education in the COVID-19 emergency.

The COVID-19 crisis has forced universities worldwide to seek urgent solutions to reconfigure traditional education programs for distance learning. The transformation process faces a number of complexities deriving from both institutional and contextual factors. It may generate threats and as well as opportunities to enhance the education system and prepare for potential future emergencies. In this article, we adopted a combined research approach to describe the experience of the  Contamination Lab of the University of Salento (CLab@Salento), an entrepreneurship education program focused on innovative and technology-based entrepreneurship for university students. Moving from the analysis of the main challenges the pandemic generated for the institution, students and faculty, we illustrate the process of redesigning the entrepreneurial learning program by leveraging digital technologies. We show a new approach to entrepreneurial storytelling, pitching and business planning and development through digital technologies. We also report the outcomes of a student survey to highlight the strengths of the redesigned program and some weaknesses, especially associated with digital technologies' limitations in education, which represent areas for future improvement. The study contributes at theory level with a new discussion on digital-supported entrepreneurship education. At practitioner level, it offers insights on redesigning traditional university programs to effectively address emergencies.

Make Your Pitch: A Flexible Assignment for Engaging Students in Aging.

Given trends in local, national, and global demographics, a need exists for educators in fields that serve older adults to provide innovative and engaging assignments that are relevant and applicable to the diverse contexts in which students are likely to find themselves as they pursue their careers. This article discusses a novel "elevator pitch" assignment that could be implemented in a number of ways to fit a variety of pedagogical methods and courses and further tailored to serve the needs of diverse student populations. For those working to actively and successfully recruit, train, and retain students in fields that serve older adults, such flexibility of assignments and delivery are likely to be acutely important as institutions, instructors, and students continue to adapt to rapidly changing developments such as those imposed by the COVID-19 pandemic.