Machine Learning for Raga Classification in Indian Classical Music

Indian Classical music consists of “Ragas”. Ragas are combinations of notes in a particular order. There are 72 combinations of these notes that form the 72 parent Ragas. The names of the Ragas are written around the circle while the defining notes of the Ragas are written inside the concentric circles. While Western music also has notes that go with the songs, it differs from Indian music in the rules that Ragas set to a specific song. For example, if someone was to perform a song in Raga #1, they must stick to the notes that are present in that Raga. Otherwise, it is no longer Raga #1. While imposing rules, Ragas also provide a lot of complexity and creativity to the artist. The artist needs to create impromptu variations while staying within the Raga’s boundaries. This difficulty gets intensified when the notes are so close to each other that they can be easily confused. Recognizing a Raga is a whole other ordeal. They can be identified only by a well-trained artist. In addition to this, every single artist has their own style which can be shown while singing or playing instruments. People add their own nuances to make the song their own, while all the time obeying the demands of the Raga. People do not sing the notes of the song. Therefore, we cannot rely on speech to recognize the Raga. People also have different baseline frequencies - child v. adult, male v. female, healthy person v. one with a cold, etc. All the complexity makes the problem of Raga identification challenging and something worth conquering. Ragas can be interpreted as specific frequency-time contours. Each peak in the frequency-time contour corresponds to a note. Each of them can be associated to a person’s mood. This can be especially useful in music recommendation systems. It can also make a huge mark in copyright violation detection systems. Machine Learning and Signal Processing tools will prove to be very effective. Signal Processing will be used to obtain the feature vector for the Machine Learning algorithm while ML will be used to classify the songs into their respective Ragas. The proposed methodology is to use Signal Processing techniques to obtain the feature vector for the ML algorithm and then use this to classify the Ragas. First, Discrete Fourier Transform (DFT) graphs are obtained. These graphs need to be normalized. The feature vectors are obtained, and these consist of smaller vectors. The vectors are the frequencies of the audio sample and its One-Hot encoded label. The Machine Learning algorithm then learns to recognize how close the vectors are to each other and classifies them into groups while outputting the name of the Raga. This method can be extended to any new Ragas that are developed

Push-Pull Factors of Undocumented Migration from Bangladesh to West Bengal: A Perception Study

Movement is an integral part of human existence. While talking about transborder migration from Bangladesh to India, we are, however, aware that this is a controversial subject. The partition of Bengal in 1947 was the cruelest partition in the history of the world and caused forced illegal migration from erstwhile East Pakistan. It is estimated that there are about 15 million Bangladeshi nationals living in India illegally. West Bengal has a border running 2,216 km along Bangladesh. The present study highlights push-pull factors of illegal Bangladeshi migration based on perceptions of respondents obtained from a qualitative survey done on the basis of purposive sampling in Kolkata and 24 parganas and two districts of West Bengal (WB), an Indian State. The economic push factors that motivate people to leave Bangladesh are instability and economic depression, poverty, lack of employment opportunity, struggle for livelihood, forced grabbing of landed property from minority group, and lack of industrialization in Bangladesh. About 56% of the respondents expressed that lack of industrialisation/lack of employment/economic insecurity would be the probable cause of this migration. Among the demographic factors, population explosion in Bangladesh and lowest human development index may be the most important cause of illegal migration from Bangladesh to West Bengal. Hindu minority group faced problems in connection with matrimonial alliances. Educational curricula, which were framed according to Islamic preaching and curtailment of facilities enjoyed by Hindu minority group, were responsible factor for illegal migration of Hindu minority population. Another cause is social insecurity. Political instability, fear of riots and terrorism in Bangladesh, inhuman attitude and activities of the political leaders, absence of democratic rights, Muslim domination, religious instigation by political leaders, insecurity feeling of Hindus, are the major crucial issues that require to be mentioned as political push factors. About 59% of the respondents are of the opinion that religious fundamentalists/insecurity of the minority group/discriminating law and order against Hindus may be the factors that motivated migration from Bangladesh to West Bengal. In terms of ethnic cleansing, one can witness elimination of groups of minorities by dominant ethnic group, curbing their rights controlling their influence in a state’s system. Double standards are observed in punishing criminals. Police officials do not record complaints from minority community. According to 85% of the respondent economic opportunity in terms of job opportunity, economic security prevailing in West Bengal worked as pull factors for migrants to West Bengal. Geographic proximity of Bangladesh and West Bengal, the linguistics and cultural similarities, same food habit, homo-ethnic climate, belief of getting shelter, cordiality, fellow-feeling, acceptance power of people of West Bengal have contributed to the movements of population from Bangladesh to West Bengal

The Effects of a Play-based Intervention on Imitation-based Rhythmic Drumming Performance in Children with Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is characterized by hallmark impairments in the social communication domain. However, there has recently been a growing recognition of the movement coordination difficulties that are also pervasive in a vast majority of this population. The Play and Move Study is an ongoing multi-site randomized controlled trial at the Universities of Connecticut and Delaware that aims to target both the perceptuo-motor and socialization impairments in children ages 6-12 with ASD. The study compares the effects of two types of whole-body, gross- motor interventions to a standard-of-care seated play intervention on multiple skills in children with ASD. Therefore, the study has 3 intervention groups, namely, a music- and dance-based “play” intervention, a general exercise-based “move” training, and a fine motor activity-based “seated play” intervention. This thesis will focus on the “play” group that receives a play-based intervention that incorporates music, yoga, dancing, and other creative movements. Drumming is one of the integral music-making activities practiced during the program to improve multi-limb coordination and rhythmic imitation capabilities of the participants. In this paper, we assessed the drumming performance of 9 participants using a custom-developed, structured coding scheme. The scheme assessed imitation error during drumming performance within 6 spatial and temporal error categories: movement precision, movement modulation, symmetry/reciprocity, pace, rhythmicity, and segmentation. A within-subjects pre-post comparison of drumming performance from before and after the 8-week intervention was conducted and we found that the participants improved in 5 out of the 6 error categories. Moreover, participants had fewer spatial than temporal errors during drumming performance. The study’s results may be used to advocate for the value of music and play-based movement interventions to improve imitation and bilateral motor coordination skills of children with ASD

Employee Attrition System Prediction using Random Forest Classifier

Despite rising unemployment, most job coverage of the COVID-19 outbreak has concentrated on layoffs. Employees have been fired for reasons related to the epidemic, which has been a less prominent issue. COVID-19 is still doing damage to the country\u27s economy. Companies are in the midst of a recession, so they are beginning to fire off unproductive employees. Making critical decisions like laying off employees or cutting an employee\u27s compensation is a challenging undertaking that must be done with extreme attention and accuracy. Adding negligence would harm the employee\u27s career and the company\u27s image in the industry. In this paper, we have predicted employee attrition using Logistic Regression, Random Forest, and Decision Tree techniques. Random Forest Classifier has outperformed other algorithms in this work. After using different machine learning techniques, we can say that Random Forest gives the best performance with a recall of 70%, and also, we have found Precision, Accuracy, and F1- Score