Statistical Analysis Of Folk Songs Of Jharkhand

Folk songs play a very significant role in Indian classical music as the root of Indian classical music is the Indian folk music itself. Different states have different folk songs. This work deals with the statistical analysis of the folk songs of Jharkhand. Each song's analysis concerns with verifying whether the probabilities of notes in the song are fixed throughout the song or are the note probabilities varying. This tells us whether the probability distribution followed by the notes is multinomial or quasi multinomial respectively. Statistical parameterization method is used to quantify melody and rhythm. The presence of rhythm and melody is also analyzed by the Inter Onset Interval (IOI) and note duration graphs. The book should be found useful by music researchers and students of music and musicology, ethnomusicologists and music enthusiasts.

Sentiment analysis and prediction of contemporary Music can have a wide range of applications in modern society, for instance, selecting music for public institutions such as hospitals or restaurants to potentially improve the emotional well-being of personnel, patients, and customers respectively. In this project, a music recommendation system is built upon a Naive Bayes Classifier trained to predict the sentiment of songs based on song lyrics alone. Online streaming platforms have become one of the most important forms of music consumption. Most streaming platforms provide tools to assess the popularity of a song in the forms of scores and rankings. In this book, we address two issues related to song popularity. First, we predict whether an already popular song may attract higher-than-average public interest and become viral. Second, we predict whether sudden spikes in the public interest will translate into long-term popularity growth. We base our findings on data from the streaming platform Billboard, Spotify, and consider appearances in its "Most-Popular" list as indicative of popularity, and appearances in its "Virals" list as indicative of interest growth. We approach the problem as a classification task and employ a Support Vector Machine model built on popularity information to predict interest, and vice versa.

The Elderly Santal Women Residing In A Village In Burdwan District Of West Bengal And Elderly Widows Residing In The (Mc Ward 87 (Kalighat) Of Kolkata Are The Subjects Of This Study. It Shows That Change In The Traditional Set Up Is The Main Cause For The Misery Of Elderly Women In Both The Areas.

Tasting Difference examines early modern discourses of racial, cultural, and religious difference that emerged in the wake of contact with foreign peoples and foreign foods from across the globe. Gitanjali Shahani reimagines the contact zone between Western Europe and the global South in culinary terms, emphasizing the gut rather than the gaze in colonial encounters. From household manuals that instructed English housewives how to use newly imported foodstuffs to "the spicèd Indian air" of A Midsummer Night's Dream, from the repurposing of Othello as an early modern pitchman for coffee in ballads to the performance of disgust in travel narratives, Shahani shows how early modern genres negotiated the allure and danger of foreign tastes. Turning maxims such as "We are what we eat" on their head, Shahani asks how did we (the colonized subjects) become what you (the colonizing subjects) eat? How did we become alternately the object of fear and appetite, loathing and craving? Shahani takes us back several centuries to the process by which food came to be inscribed with racial character and the racial other came to be marked as edible, showing how the racializing of food began in an era well before chicken tikka masala and Balti cuisine. Bringing into conversation critical paradigms in early modern studies, food studies, and postcolonial studies, she argues that it is in the writing on food and eating that we see among the earliest configurations of racial difference, and it is experienced both as a different taste and as a taste of difference.

The book opens with a short introduction to Indian music, in particular classical Hindustani music, followed by a chapter on the role of statistics in computational musicology. The authors then show how to analyze musical structure using Rubato, the music software package for statistical analysis, in particular addressing modeling, melodic similarity and lengths, and entropy analysis; they then show how to analyze musical performance. Finally, they explain how the concept of seminatural composition can help a music composer to obtain the opening line of a raga-based song using Monte Carlo simulation. The book will be of interest to musicians and musicologists, particularly those engaged with Indian music.