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Speech coding plays a significant role in voice communication and improving network bandwidth efficiency for applications that require long-distance communication or storage space utilization. Non-uniform sampling (NUS) is a technique for the same, which performs data reduction by sampling at irregular intervals. In the literature, researchers use the structural property of the speech waveform for studying various NUS methods, such as LCSS, MMD, IPD, and zero-crossing point. However, in this paper, we consider the speech signal’s statistical properties to propose an optimal NUS approach. The proposed technique statistically analyzes the speech signal to sample the abrupt changes over a time frame and approximates the signal with minimal reconstruction error using cost and linear penalty functions for avoiding the over-fitting problem. The proposed technique further performs the optimization using the branch-and-bound. To evaluate the proposed NUS, we design a speech waveform encoder called Block Adaptive Amplitude Sampling (BAAS). A BAAS encoder can directly perform statistical analysis on the speech waveform to select data samples corresponding to the most significant changes in the signal. The decoder approximates the eliminated values using linear interpolation. We experimentally study the proposed technique using various matrices and measures such as POLQA and MUSHRA test. The evaluation shows that the proposed NUS technique retains only 25% of data samples to get an acceptable quality signal regeneration. In addition, comparative studies with MMD and IPD show that the proposed algorithm performs 1.6% better with 30% lower MSE scores.

India’s urbanization rate has increased from 26% in 1991 to 35% in 2021, and it is expected to reach 41% by 2030 (World Bank 2022). In response to this, as the backbone of its growing economy, the country’s infrastructure development, particularly transportation development, has been heavily emphasized. The railway sector will see an investment of $715.41 billion by 2030. Along with a series of supporting government policies, an opportunity to expand the high-speed rail (HSR) network has been presented in India. From the viewpoint of the government, HSR is an effective tool to address the surge in travel demand because of its mass transportation, high speed, and high energy efficiency (Suzuki et al. 2022). Besides, HSR projects bring about a ripple effect on the Indian economy by generating local employment opportunities, catalyzing the steel and cement sector and galvanizing AatmaNirbhar Bharat (an initiative by the Government of India to make the country self-reliant) through the indigenization of technology (National High Speed Rail Corporation Limited 2022). From the perspective of communities and individuals, HSR improves their quality of life by offering a faster and safer transportation option for work and travel.

India being the largest democracy of the world and being one of the fastest growing economy in the world is facing the problem of unemployment from a long period of time. The problem was worst during the post-independence era and after independence various schemes and necessary provisions are taken by the government to tackle this.The government has launched various self-employment as well as credit linked schemes to assist people in starting their own business. This paper attempts to examine the status of unemployment its causes, steps taken by the government regarding this along with suggesting measures to overcome this problem.

Oral and maxillofacial surgery (OMFS) is a specialty of dentistry dealing with the surgical aspects of head and neck. It is considered to be a bridge between medical and dental specialties. However, little is known regarding this field among the medical practitioners. Method: A survey was conducted among 200 health care professionals in KANPUR to check for awareness regarding the field of OMFS, and its scope in the medical field. Result: Most of the dental professionals were found to be more aware of range of work done in Oral and Maxillofacial Surgery, in contrast to medical professionals. The difference in perceived knowledge was not significant for nose fractures, cleft lip and/or palate, removal of wisdom tooth and temporomandibular joint problems. Most of the respondents referred cases to OMFS for mandibular, maxillary and zygoma fractures. OMFS was also preferred for dento-alveolar trauma, maxillary cysts, benign mandibular tumors, removal of wisdom tooth, orthognathic surgeries, TMJ problems and maxillofacial infections. There was a difference of opinion for others. Dental professionals preferred OMFS for biopsy of oral lesions, lump in the mouth, mandibular reconstruction and bone graft in mandible, while medical professionals referred less number of cases for these conditions. There were more referrals to OMFS from medical professionals’ side for dental implants, as compared to dental professionals. There were overall less number of referrals to OMFS for cancer of the mouth, removal of salivary glands, cleft lip and/or palate, and problems with facial appearance. Conclusion: The knowledge of health care professionals regarding specialty referrals in oro-facial conditions is average. Many health care professionals still associate our specialty mostly with intraoral conditions and tooth removal only. Health care professionals still lack information regarding the advances and new procedures being done in Oral and Maxillofacial Surgery, Dental Practitioners were more aware about the range of work done and referrals to OMFS than Medical Practitioners of all specialties

Objectives In India, although nutritional conditions have improved, a high prevalence of anthropometric failure is still reported in children. Unfortunately, there are knowledge gaps surrounding nutrient and anthropometric shortcomings as well as dietary patterns. More than half of children are consuming a vegetarian diet. This study aimed to evaluate the effects of dietary adequacy levels on anthropometric failure in 5772 vegetarian children (ages 6–23 mo) satisfying minimum dietary diversity. Methods Data were collected from the National Family Health Survey 2015–16. We created three food combinations: maximum adequacy (dairy and four food groups), medium adequacy (dairy and three food groups), and minimum adequacy (four food groups excluding dairy). We calculated odds ratios (ORs) for the association between dietary adequacy levels and anthropometric failures with 95% confidence intervals (CIs) using logistic regression models. The modification effect of breastfeeding status was also explored. Results Approximately 35% of children had stunted growth. ORs showed a significant increase in the risk of anthropometric failure, notably wasting, and underweight in children with minimum-adequacy diets (OR, 1.46; 95% CI, 1.15–1.86) compared with children with maximum-adequacy diets. Those associations were more pronounced among children who were not breastfed (OR, 2.44; 95% CI, 1.19–5.00) than among children who were (OR, 1.35; 95% CI, 1.04–1.74; P value for the interaction = 0.07). Similar associations were observed for wasting among children who were not breastfed (OR, 2.82; 95% CI, 1.34–5.95) and who were breastfed(OR, 1.12; 95% CI, 0.85–1.5; P value for the interaction = 0.03). Conclusions Dairy is an essential source of nutrients that are required for healthy growth and development in children younger than 2 y, even if they satisfy minimum dietary diversity conditions in India.

The stylization of pitch contour is a primary task in the speech prosody for the development of a linguistic model. The stylization of pitch contour is performed either by statistical learning or statistical analysis. The recent statistical learning models require a large amount of data for training purposes and rely on complex machine learning algorithms. Whereas, the statistical analysis methods perform stylization based on the shape of the contour and require further processing to capture the voice intonations of the speaker. The objective of this paper is to devise a low-complexity transcription algorithm for the stylization of pitch contour based on the voice intonation of a speaker. For this, we propose to use of pitch marks as a subset of points for the stylization of the pitch contour. The pitch marks are the instance of glottal closure in a speech waveform that captures characteristics of speech uttered by a speaker. The selected subset can interpolate the shape of the pitch contour and acts as a template to capture the intonation of a speaker’s voice, which can be used for designing applications in speech synthesis and speech morphing. The algorithm balances the quality of the stylized curve and its cost in terms of the number of data points used. We evaluate the performance of the proposed algorithm using the mean square error and the number of lines used for fitting the pitch contour. Furthermore, we perform a comparison with other existing stylization algorithms using the LibriSpeech ASR corpus.

The target of this inspection is exploring the influence of Medical Course training on satisfaction, loyalty, and Word-Of-Mouth (WOM) in Indian Medical Colleges context. A twenty-five items Questionnaire (CEQ) was distributed in Medical Colleges in Delhi. Demographic variables were gender, age, education level, preferred Source of Study, Hostel Inhabitant, Family income, Class Attendance, Personality Type and the distance between College and residence. Linear regression analysis was performed for this research. Responses were collected both through online and offline means. Results suggest that improving course experience of education influences its outcomes for the institute.

The nutritive quality of legumes is poor due to deficiency of certain amino acids, low protein and starch digestibility and presence of certain anti-nutritional factor. As a result, processing techniques are adopted to overcome these problems and increases the palatability of legumes. Malting is one of the simple and inexpensive traditional processing techniques that are widely accepted for achieving desirable changes in the composition of legumes. In view of the above aspects the aim of present work was to investigate the effect of malting on nutritional profile of alfalfa seeds and development of value added fermented products. The nutritional profile (moisture, ash, protein, fat, fibre, carbohydrate, calcium and iron content) of unmalted and malted alfalfa flours were analysed with standard protocols. Fermented products viz; Dhokla and Appe were developed by incorporating malted alfalfa flour (MAF) at 10%, 15% and 20% level and organoleptic evaluated by 5-point composite Score and 9-point hedonic scale. The study data revealed that malted alfalfa flour contain significantly increased content of protein (19.6%), fibre (29.1%), iron (39.9%) and calcium (93.9%) when compared to unmalted alfalfa flour at P≥0.05 level. Sensory evaluation showed that fermented products developed at 10% level of incorporation of malted alfalfa flour were highly acceptable and registered insignificant difference at P≤ 0.05 level when compared with their respective standard products. Thus, it can be concluded that the malted alfalfa seeds contain appreciable amount of nutrients that may be used in the formulation of fermented food products and advantageous in human health.

Automation is an important part of 21st century. We can control many household appliances like light, door, fan, AC etc. We can also use it for security and emergency applications it basically saves time and in-creases energy efficiency. Home Automation is mostly helpful to handicapped and old aged people because it allows them in critical situations to control home appliances. Nowadays home automation and internet of things are gaining popularity because automatic systems are more preferred over manual systems Smart home is mainly used to make life easier IoT generator using count of data it contains large amount large number of sensors and the data which can be controlled for monitoring objects this paper basically represents how machine learning and data mining can be used for converting the normal Smart home to an automated home. User can control their home using their smartphones as well as computer or any devices which is which has internet connectivity it also used as many prediction algorithms for reminders and alerts additional system interaction with the user.

Counting the number of speakers in an audio sample can lead to innovative applications, such as a real-time ranking system. Researchers have studied advanced machine learning approaches for solving the speaker count problem. However, these solutions are not efficient in real-time environments, as it requires pre-processing of a finite set of data samples. Another approach for solving the problem is via unsupervised learning or by using audio processing techniques. The research in this category is limited and does not consider the large-scale open set environment. In this paper, we propose a distributed clustering approach to address the speaker count problem. The separability of the speaker is computed using statistical pitch parameters. The proposed solution uses multiple microphones available in smartphones in a large geographical area to capture and extract statistical pitch features from the audio samples. These features are shared between the nodes to estimate the number of speakers in the neighborhood. One of the major challenges is to reduce the error count that arises due to the proximity of the users and multiple microphones. We evaluate the algorithm’s performance using real smartphones in a multi-group arrangement by capturing parallel conversations between the users in both indoor and outdoor scenarios. The average error count distance is 1.667 in a multi-group scenario. The average error count distances in indoor environments are 16% which is better than in the outdoor environment.

In today’s digital world, security plays an important role in the field of web applications. CAPTCHA is a challenge reaction test that segregates human from machine based automatic program that may harm data credentials. It is required to protect the website or user data from different kind of machine attacks. To ensure the security, CAPTCHA is a tool through which a turing test can be performed that denied robots to intervene with the systems. CAPTCHA stands for Completely Automated Public Turing Test to tell Computers and Human Apart. Machine based attacks can harm the database by enrolling junk entries and an account can be hacked through brute force attack. By having these problems on mind, there are so many CAPTCHA techniques have been introduced such as text based CAPTCHA that involves 3D text, moving letters, distorted text and other than the text based CAPTCHA there are also graphical CAPTCHA, Audio CAPTCHA. One Time Password based CAPTCHA have been carried out for better security concerns. So, there are so many ideas have been developed till now which claim better security approach and the motive of this paper is to review the previously proposed systems that may have some bugs and complicated for human to solve in less time. The paper also emphasizes the proposed solution or system that replaces the existing system and provides better level of accuracy.

Voice transformation converts the voice of a speaker to an intended person, such that the listener is deceived for the target speaker. Voice transformation has many application like low bandwidth speech encoding, voice disguise, entertainment, voice morphing. The proposed methodology transforms the pitch contour of one speaker to another by segmental linear mapping of the pitch contour. This method considers sound as an one-dimensional continuous signal. A linear mapping function is considered to transform pitch contour i.e. y(t) = f(x(t)), where y(t), x(t) are two different pitch contours of sound signal and f is the mapping function. The segmentation of the pitch contour is done using linguistically motivated parameters, which are extracted automatically from the audio signals to capture the inotation of the voice. The proposed methodology do not need any large speech corpus for training or learning the speech intonation generation, making the methodology computationally efficient. The simulation result proves the efficiency of the transformation of the pitch contour.

Speech is embedded with multiple information which includes the message to be transferred, the speaker information and the prosodic information. The physical properties of sound waves, such as mel-frequency cepstral and linear predictive cepstral coefficients are used in the speaker recognition system. While the prosodic features, like pitch and intensity, plays a vital role in human speech perception. In this paper, we investigate the speaker separability problem in real-time by clustering the statistical pitch feature. The goal is to identify the number of speakers in a given voice sample. For this, we analyze the space distribution of the prosodic parameter (pitch) to form clusters using the statistical parameters: mean, standard deviation, skewness and kurtosis. We experimentally observe that the statistical parameter belonging to a voice sample lie in the range of similarity. We calculate the variability for multiple speaker clusters and found that the intra cluster distance is 13 units, whereas the inter cluster distance varies according to the number of users in the voice sample.