Background and aims: Covid-19 virus started from Wuhan, China and has brought the world down to its knees. It has catapulted as a venomous global phenomenon. This study focuses on the Covid-19 situation in India and its recovery time. Method: The study period is from March 1, 2020 to April 25, 2020. A random sample of 221 individuals found positive with Covid-19 from March 1, 2020 to 31st March is included in the study which is followed up April 25, 2020. There is a male preponderance in the sample with 66% of the Covid-19 patients being male and about 34% being female. Kaplan-Meier Product limit estimator, Kaplan-Meier survival curve and Log-rank test are used to analyze the recovery time of Covid-19 patients. Result: From the results of the study, it is found that the average recovery time of Covid-19 patients in India is 25 days (95% C.I. 16 days to 34 days). Only 4% of the patients get cured after 10 days of treatment. The recovery time of male and female patients is not statistically different. Recovery time of patients belonging to different age groups is also not statistically significant. Conclusion: This information on recovery time of Covid-19 patients will help planners to chalk out effective strategies.

Raaga is the heart of Indian Classical Music. A raaga is an arrangement of 12 notes in the octave. Like Indian classical music, Sankari Sangeets which are composed 500 years ago are also based on raaga. The musicologist of Sankari Sangeet found nearly 26 different raagas in Sankaree Sangeet. Each of these raagas has some specific characteristics such as Aruhana (Ascending order of notes), Avaruhana (Descending order of notes), Pakad, Time of Singing, Vadi swara (Dominant/Important Note), Samvadi swara (Second Dominant Note), etc. This piece of work considers all of the 26 raagas of Sankari Sangeet to statistically cluster on the basis of 18 features extracted from each raaga. For clustering of the raagas, k-prototypes clustering technique is used which gives four clusters of sizes 9, 4, 6, and 7. Also, a comparison is carried out between the clusters obtained from k-prototypes clustering and existing theoretical Thaat-based classification.

Objectives: A melody is made up of several musical notes or pitches that are joined together to form one whole. This experiment aims to develop four models based on the Mel-frequency Cepstral Coefficients (MFCC) to classify the melodies played on harmonium corresponding to five different class of Assamese folk Music. Methods: The melodies of five different categories of Assamese folk songs are selected for classification. With the help of expert musicians, these melodies are played in harmonium and audio samples are recorded in the same acoustic environment. 20 MFCC's are extracted from each of the samples and classification of the melodies is done using four supervised learning techniques-Decision Tree Classifier, Linear Discriminant Analysis (LDA), Random Forest Classifier, and Support Vector Machine (SVM). Findings: The performance of the fitted models are evaluated using different evaluation techniques and presented. A maximum of 94.17% average accuracy score is achieved under Support Vector Machine. The average accuracy scores of Decision Tree Classifier, Linear Discriminant Analysis (LDA), and Random Forest Classifier are 73.58%, 85.58%, and 86.11% respectively. The models are developed based on 250 samples (50 from each type). However, increasing the training sample size, there is a possibility to improve the performances of the other three models also. Novelty: The developed approach for identifying the melodies is based on computational techniques. This work will certainly provide a basis for conducting further computational studies in folk music for any community.