Music Similarity Estimation

Music is a complicated form of communication, where creators and culture communicate and expose their individuality. After music digitalization took place, recommendation systems and other online services have become indispensable in the field of Music Information Retrieval (MIR). To build these systems and recommend the right choice of song to the user, classification of songs is required. In this paper, we propose an approach for finding similarity between music based on mid-level attributes like pitch, midi value corresponding to pitch, interval, contour and duration and applying text based classification techniques. Our system predicts jazz, metal and ragtime for western music. The experiment to predict the genre of music is conducted based on 450 music files and maximum accuracy achieved is 95.8% across different n-grams. We have also analyzed the Indian classical Carnatic music and are classifying them based on its raga. Our system predicts Sankarabharam, Mohanam and Sindhubhairavi ragas. The experiment to predict the raga of the song is conducted based on 95 music files and the maximum accuracy achieved is 90.3% across different n-grams. Performance evaluation is done by using the accuracy score of scikit-learn

Is That Twitter Hashtag Worth Reading

Online social media such as Twitter, Facebook, Wikis and Linkedin have made a great impact on the way we consume information in our day to day life. Now it has become increasingly important that we come across appropriate content from the social media to avoid information explosion. In case of Twitter, popular information can be tracked using hashtags. Studying the characteristics of tweets containing hashtags becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, and sentiment analysis among others. In this paper, we have analyzed Twitter data based on trending hashtags, which is widely used nowadays. We have used event based hashtags to know users' thoughts on those events and to decide whether the rest of the users might find it interesting or not. We have used topic modeling, which reveals the hidden thematic structure of the documents (tweets in this case) in addition to sentiment analysis in exploring and summarizing the content of the documents. A technique to find the interestingness of event based twitter hashtag and the associated sentiment has been proposed. The proposed technique helps twitter follower to read, relevant and interesting hashtag.Comment: 10 pages, 6 figures, Presented at the Third International Symposium on Women in Computing and Informatics (WCI-2015

Functional Renormalization Description of the Roughening Transition

We reconsider the problem of the static thermal roughening of an elastic manifold at the critical dimension $d=2$ in a periodic potential, using a perturbative Functional Renormalization Group approach. Our aim is to describe the effective potential seen by the manifold below the roughening temperature on large length scales. We obtain analytically a flow equation for the potential and surface tension of the manifold, valid at all temperatures. On a length scale $L$, the renormalized potential is made up of a succession of quasi parabolic wells, matching onto one another in a singular region of width $\sim L^{-6/5}$ for large $L$. We also obtain numerically the step energy as a function of temperature, and relate our results to the existing experimental data on $^4$He. Finally, we sketch the scenario expected for an arbitrary dimension $d<2$ and examine the case of a non local elasticity which is realized physically for the contact line.Comment: 21 pages, 2 .ps figures. Submitted to E.P.J.

India Transformed? Insights from the Firm Level 1988-2005

Using firm-level data this paper analyzes, the transformation of India's economic structure following the implementation of economic reforms. The focus of the study is on publicly-listed and unlisted firms from across a wide spectrum of manufacturing and services industries and ownership structures such as state-owned firms, business groups, private and foreign firms. Detailed balance sheet and ownership information permit an investigation of a range of variables such as sales, profitability, and assets. Here we analyze firm characteristics shown by industry before and after liberalization and investigate how industrial concentration, the number, and size of firms of the ownership type evolved between 1988 and 2005. We find great dynamism displayed by foreign and private firms as reflected in the growth in their numbers, assets, sales and profits. Yet, closer scrutiny reveals no dramatic transformation in the wake of liberalization. The story rather is one of an economy still dominated by the incumbents (state-owned firms) and to a lesser extent, traditional private firms (firms incorporated before 1985). Sectors dominated by state-owned and traditional private firms before 1988-1990, with assets, sales and profits representing shares higher than 50%, generally remained so in 2005. The exception to this broad pattern is the growing importance of new and large private firms in the services sector. Rates of return also have remained stable over time and show low dispersion across sectors and across ownership groups within sectors.