So, finally ICSE and ISC Results Out this year.

Like the last few years, this year too girls have outshone boys in the Indian Certificate of Secondary Examination (ICSE) Class 10 and Indian School Certificate (ISC) Class 12 examinations.

Considering the overall India numbers, for ICSE the pass percentage of girls stands at 98.95%, while for boys it is 98.12%. For ISC too, girls have achieved a pass percentage of 97.49% and boys have scored 95.27%.

Check the official results here. http://cisceresults03.azurewebsites.net/Results/Result/ViewResult

In total, 1,58,833 appeared for the Class 10 exam and 71,141 students appeared for Class 12 board exam conducted by the Council for Indian School Certificate Examination (CISCE). Among them for ICSE, 70,624 were girls and 88,209 were boys, and for ISC, 32,482 were girls and 38,659 were boys.

Similarly, the overall pass percentage for the country has gone up by 0.21% for ICSE and 1.01% for ISC.

“Compared to the last year, the pass percentage in both the results have gone up this time and also toppers have broken the past five year record,” said  Gerry Arathoon, CISCE chief executive and secretary.

Even though the all-India topper for ISC is Arkya Chatterjee, a boy from Vivekananda Mission School Kolkata, in the second position where four students have scored the same 99.50%, three of them are girls.

Similarly, 13 students have secured the third position at 99.25%, which includes 9 girls.

In ICSE, the three toppers — Sougata Chowdhury from St Xavier’s Collegiate School, Kolkata, Ananya Harshad Patwardhan from Chatrabhuj Narsee Memorial School, Mumbai and Tezan Tapan Sahu from St Mary’s ICSE school, Navi Mumbai —  scored 99.20%, one among them is a girl.

Similarly, in the second position, three students scored 99%, and one of them is a girl. In the third position too, 11 students scored 98.80%, and four among them are girls.

Following all-India trend, in Delhi-NCR too, girls have outperformed boys. In ICSE, girls recorded a pass percentage of 99.14% as against 97.94% of boys. In ISC too, the pass percentage for girls stands at 97.64%, and for boys it is 96.88%.

However, the Class 12 topper in Delhi NCR is a boy: Rupal Goyal from Holy Child School, Ghaziabad, scored 99%. Even in ICSE, the topper is Yashvardhan Tomar, from St Mary’s Academy Meerut Cantt.

In West Bengal, which has the maximum toppers, girls have outshone boys. For ICSE, girls have a pass percentage of 98.54% and for boys it stands at 97.86%. In ISC, too, this figure stands at 97.24% for girls, and for boys it is much lower 94.29%.

It’s the same story in Maharashtra and Karnataka too: In both these states, girls have recorded a better pass percentage than boys.

In Maharashtra, girls have a pass percentage of 99.77% in ICSE, while for boys it stands at 99.69%. Similarly in ISC, it is 99.27% for girls, and 98.32% for boys.

In Karnataka, girls recorded a pass percentage of 99.75% in ICSE as against the 99.59% of boys. In ISC, the figure stands at 99.43% for girls. Boys scored a much lower pass percentage of 97.86% in the exams.

When considered region wise, south has the best pass percentage at 99.66% followed by the west at 99.55% in ICSE. It’s the same story in ISC: South recorded the best pass percentage 99.08%, followed by the west at 97.47%.

The south has the highest percentage of girls. For ICSE, 49.43% girls appeared, and for ISC 49.49% gave the examination.

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Two basic technologies in mobile phones, CDMA and GSM represent a gap you can’t cross. They’re the reason you can’t use many Reliance[CDMA] phones on Vodafone’s network and vice versa. But what does CDMA vs. GSM really mean for you?

CDMA (Code Division Multiple Access) and GSM (Global System for Mobiles) are shorthand for the two major radio systems used in cell phones. Both acronyms tend to group together a bunch of technologies run by the same entities.

Most of the world uses GSM nowadays. The global spread of GSM came about because in 1987, Europe mandated the technology by law, and because GSM comes from an industry consortium. What we call CDMA, by and large, is owned by chipmaker Qualcomm. This made it less expensive for third parties to build GSM equipment.

For call quality, the technology you use is much less important than the way your carrier has built its network. There are good and bad CDMA and GSM networks, but there are key differences between the technologies.

It’s much easier to swap phones on GSM networks, because GSM carriers put customer information on a removable SIM card. Take the card out, put it in a different phone, and the new phone now has your number. What’s more, to be considered GSM, a carrier must accept any GSM-compliant phone. So the GSM carriers don’t have total control of the phone you’re using.

That’s not the case with CDMA. In the U.S., CDMA carriers use network-based white lists to verify their subscribers. That means you can only switch phones with your carrier’s permission, and a carrier doesn’t have to accept any particular phone onto its network. It could, but typically, U.S. carriers choose not to.

3G CDMA networks (known as “EV-DO” or “Evolution Data Optimized”) also, generally, can’t make voice calls and transmit data at the same time. Once more, that’s an available option (known as “SV-DO” for “Simultaneous Voice and Data Optimization”), but one that U.S. carriers haven’t adopted for their networks and phones.

On the other hand, all 3G GSM networks have simultaneous voice and data, because it’s a required part of the spec. (3G GSM is also actually a type of CDMA.)

So why did so many U.S. carriers go with CDMA? Timing. When Verizon’s predecessors and Sprint switched from analog to digital in 1995 and 1996, CDMA was the newest, hottest, fastest technology. It offered more capacity, better call quality and more potential than the GSM of the day. GSM caught up, but by then those carriers’ paths were set.

It’s possible to switch from CDMA to GSM. Bell and Telus in Canada have done it, to get access to the wider variety of off-the-shelf GSM phones. But Verizon and Sprint are big enough that they can get custom phones built for them, so they don’t see the need to waste money switching 3G technologies when they could be building out their 4G networks.

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When music’s natural soft-to-loud dynamics are squashed flat music sounds louder, but is louder better?

Dynamic range compression (DRC) or simply compression reduces the volume of loud sounds or amplifies quiet sounds by narrowing or “compressing” an audio signal’s dynamic range. Compression is commonly used in sound recording and reproduction and broadcasting and on instrument amplifiers.

Dynamic range compression isn’t new, it’s been used by recording, mixing, and mastering engineers for many decades. A little bit of compression is fine, but over-compression can sound downright ugly. Most of today’s music, whether it’s on LP, CD, the radio or iTunes is over-compressed. Most remastered CDs are over-compressed.

Before we go any further, I’m not referring to the lossy compression used in MP3s, or lossless compression used in Apple Lossless. They’ve got their own set of problems, but dynamic range compression is a very different predicament.

To the casual listener compression can sound “good,” mostly because it makes the music seem louder and punchier, and once music’s natural soft-loud dynamic shifts are squashed flat music is easier to hear in noisy environments like cars or over iPods. Compression reduces the need to adjust playback volume–because it’s always nearly the same volume–loud.

Engineers worry that if they don’t compress their recordings the music would seem too soft and low. That is, if a music listener went from really loud, compressed music to quieter, uncompressed music they probably wouldn’t like uncompressed music–unless they turned it up! That way they would hear the music’s natural soft-loud dynamics.

Unfortunately, that’s not an acceptable scenario to most engineers or record labels. They’re addicted to over-compression, it’s a hard habit to break.

But the unnatural onslaught of compressed sound obliterates musical nuance, delicacy and emotional power. Compression’s loud-all-the-time nature sucks the life out of music.

DRC has been around for a long time, and while the practice is as bad as it has ever been, the past few years have seen the beginnings of a backlash by those who notice how bad the music sounds from artists they love.

What are your thoughts? Do you care about compression? Would you be interested in better-recorded music from your favorite artists? With digital distribution the norm, it wouldn’t be overly difficult to offer a lesser-compressed version of tracks for fans in the know. Forget high-resolution audio, better-mastered audio might be a nice first step. Who knows, maybe this is one of the reasons why there has been a resurgence of vinyl.

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There are two kinds of audio compression, and they’re not the same. But people get them confused all the time, and it’s driving me nuts.

Imagine you’re holding a sponge.Now squash it in your fist.

When you squash the sponge, it takes up less space. The more you squash it, the less like a sponge it looks. But when you release it it springs back again, unharmed.

You can do the same thing to audio – that’s audio compression. Good old-fashioned audio compression, as has been widely used in pop music since the 60s at least – The Beatles started using it on Paul’s bass around the time of “Paperback Writer” and from then onwards, for example.

Using a compressor on audio does the same thing as your fist does to the sponge – it squashes it into a smaller space, so it can be packed more densely. When you record a compressed audio signal it’s like taking a photograph of the squashed sponge – the results become “locked in”. If you squash it so much that it doesn’t “look like a sponge” any more and then record it, you’ve dramatically changed the sound of the original audio.

This is known as ‘dynamic range compression'[DRC], and it’s used to control very lively sounds (like the human voice, or a bass guitar) to reduce contrast – originally to make the most of the limited “headroom” on radio and vinyl, and often as a creative effect.

In the past this was all done with analogue electronics – valves and transistors – but nowadays you can get digital audio compressors that do the same thing. Musicians and sound engineers argue about whether digital compressors sound as good as analogue compressors, but they do the same job.

If you overdo compression, it often sounds bad – and modern digital compressors have enabled people to take that to a whole new level. But that’s a whole other discussion…

Back to the sponges

Now get your sponge, and cut it up with scissors.

It no longer looks like sponge, right ?

BUT (and this is where the sponge analogy breaks down a little, I admit) if you had some magic “sponge glue”, you could re-assemble the sponge to it’s original self again, if you were careful about how you labelled the pieces in the first place.

Now of course there’s no such thing as “magic sponge glue” in the real world – that would just be silly (!) – but in the digital world of computers, there absolutely is.

Once the sponge (or the music, are you still with me ?) is recorded as digital information, it can be chopped up, re-packed into half the original size, sent halfway round the world and re-assembled again – perfectly. No magic sponge glue is required – the reconstructed digital sponge is identical to the original digital sponge.

This what happens when you make a zip file of something before you email it.

It’s called lossless data compression – “compression” because the original digital items data is re-packed into a smaller space, and “lossless” because nothing gets damaged along the way – but it’s an entirely digital process, and it’s nothing like dynamic range compression (sponge squashing) at all – either the analogue or the digital variety.

Here’s where it gets really messy for the sponge

NOW imagine that instead of cutting the sponge up with scissors, you gouge it’s heart out.

(Relax, sponge-fans, this is just a thought experiment ! No sponges were harmed during the writing of this post.)

Seriously – cut a small hole in the sponge and pick all the insides out, until you’re left with only a thin outer shell. If you’re careful, you’ll end up with something that still looks like the original sponge. It’s had it’s heart ripped out, true – but it still looks OK.

The digital equivalent of this process is also known as data-compression – but it’s different to the “chop-up-and-reassemble” variety, because at the end of the process, 90% of the original sponge is missing.

This is what happens when you make a jpeg or mp3 file, and it’s called lossy data compression. “Compression” because again, it’s been squashed into a (much) smaller space, and “lossy” because something has been lost – the 90%.

It’s important to notice that the process is permanent – once the heart of the sponge is thrown away, you can’t put it back. So, you need to be careful which bits of the sponge you pick out, and not to take too much away. In the same way, it’s vital to make a good job of using lossy compression on your audio when encoding an mp3 file.

The chances are though, looking at the digital sponge after lossy compression, you won’t spot the difference – in the same way that many people don’t notice the quality lost in an mp3 encode.

What does all this have to do with Neil Young, Bob from St Etienne and the PM show ?

Both over-squashing a musical sponge, and gouging the heart out of it, damage the sponge.

In the same way, both over-compressing music when recording it, and mp3 encoding it, can spoil the music.

And both of these happen all the time, at the moment – music is hugely over-compressed in futile attempts to win the so-called Loudness War – and most people listen to music that has been through lossy compression almost exclusively.

Digital TV uses lossy data-compression, digital radio uses it, mp3 files use it, the iTunes store uses it, Spotify uses it…

So, both the compression have made listening to music more easy and better upto a certain extent, but over-compression is taking the life out of the music, and the sponge.

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Anyone who has studied for anything will likely relate to the idea of a ‘study playlist’ – a set of tracks you put on to get yourself into the mood to study. If you take a deeper look at those playlists, you’ll find that the majority of them consist of instrumental or electronic productions, but why is that?

Unsurprisingly,music with lyrics can be distracting. In an recent experiment conducted by Carol A. Smith and Larry W. Morris of Middle Tennessee State University, “it was revealed that students who listened to their preferred type of lyrical music whilst taking a series of tests did not perform as well as did students who listened to sedative music.”

In the same way, “sedative” music is better for cognitive function than aggressive music or no music at all.

A study conducted in 2002 exposed children of ages seven to ten to calm, relaxing music as they took a series of arithmetic and memory tests, and then to loud, aggressive music while they took similar tests. Afterwards the children took the same tests in silence. The results showed that calming music greatly enhanced test-taking abilities, while aggressive music served as a distraction. However, this study neglected to take into account different personality types, thus it is yet unknown if this affects both introverts and extroverts equally.

Now, none of these “discoveries” are groundbreaking in any way. A lot of experiments, and science in general, are conducted to confirm hypotheses, many already accepted as truth. What these results will do is give you an excuse to keep blasting dubstep when your roommate complains.

Now, if you’re thinking where to find such music, here’s a quicklist.

somafm.com offers free streaming music and a iPhone app as well as apps for other types of phones. Suggested playlists drone zone are(Served best chilled, safe with most medications. Atmospheric textures with minimal beats.) and secret agent(The soundtrack for your stylish, mysterious, dangerous life. For Spies and PIs too!). Most music players are supported. I know it works with iTunes.

di.fm– Digitally Imported offers free streaming electronic and dance music.

If you are a fan of Pandora (who isn’t) try out a playlist created by a Pandora employee Michael Zapruder called Ovals.

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As more and more devices are getting access to the web the data produced has also increased enormously. Of all the total data produced till now 90% of it is produced in last two years, this stat itself shows how the revolution of internet is producing vast amount of data which if used effectively can do wonders. People now-a-days communicate, participate on many social websites, blogs, forums etc. from which can offer great opportunity to analyze the data, apply theories, algorithms and technologies that search and extract relevant data from huge quantities of data available from various websites and mine them for opinions thereafter. Data analysis is widely growing as a field and sentimental analysis is an important feature involved in it. Sentimental analysis is basically determining the attitude/ judgment/ evaluation/ emotional state or intended emotional communication of the speaker or the writer with the use of natural language processing, text analysis, computational logistics and various algorithms.

The World Wide Web is growing at an alarming rate not only in size but also in the types of services and contents provided. Each and every users are participating more actively and are generating vast amount of new data. In this era of automated systems and digital information every field of life is evolving rapidly and generating data because of which huge amounts of data produced in field of science, engineering, medical, marketing, finance etc. Automated systems are needed automated analysis and classification of data which help to take enterprise level decisions.

There are three main classifications levels in sentiment analysis: 1. Document level classification. 2. Aspect level classification.3. Sentence level classification Document-level aims to classify an opinion document as expressing a positive or negative opinion or sentiment. It considers the document a basic information unit. Sentence level aims to classify the sentiment expressed in each sentence. However, there‟s not much difference between document level and sentence level because sentence are just short documents.

Point-wise Mutual Information (PMI) is a basic example of Sentiment Analysis.

The mutual information measure provides a formal way to model the mutual information between the features and the classes. This measure was derived from the information theory. The point-wise mutual information (PMI) Mi(w) between the word w and the class iis defined on the basis of the level of cooccurrence between the class iand word w. The expected co-occurrence of class iand word w, on the basis of mutual independence, is given by Pi * F(w), and the true co-occurrence is given by F(w) *pi(w). The mutual information is defined in terms of the ratio between these two values and is given by the following equation: Mi(w) =[ log (F(w) * Pi (w) / F(w) * Pi ] = log [ pi (w) / Pi]

The word w is positively correlated to the class i, whenMi(w) is greater than 0. The word w is negatively correlatedto the class iwhen Mi(w) is less than 0. PMI is used in many applications like developing a contextual entropy model to expand a set of seed words generated from a small corpus of stock market news articles. Their contextual entropy measures the similarity between two words by comparing their contextual distributions using an entropy measure allowing discovery of words similar to seed words. Once the seed words has been expanded words are used to classify the sentiments of new articles.

Sentiment mining research is of utmost importance not only for commercial establishments but also for the common man. With the World Wide Web offering various ideas and opinions it is very important to be aware of the malicious opinions also.This also opens a new challenge to researchers in order to build lexica, corpora and dictionaries resources for other languages.

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Electronic dance music has been on the rise for the past decade and is becoming a trend amongst the youth. More and more neophytes are inspired to produce dance music and its production ahs become easier than ever, due to the availability of free DAW’s[Digital Audio Workstations] and a number of VST’s[Virtual Studio Technology]. Thus, due to this enormously increased competition and a huge availability of these tracks, predicting a hit track by listening to each of them has become virtually impregnable. The record labels thus have to invest enormous amount of money in AnR to quest for the producers who can produce marketable tracks. In 201, music record labels invested a total of 6.5 billion in search of new talent world wide.

Gaining insight of what actually makes a song popular would make finding the hit factor simpler.  Research has been going on for predicting this factor digitally with the help of software, and thereby predict the next hit track. A recent ongoing research by some graduates have utilized various musical as well as practical parameters like tempo, loudness, energy, etc. to predict whether a newly composed song would be popular or not. This is accomplished  by analysing  them with the corresponding parameters – obtained using various algorithms – of the top 40 most popular tracks each month for  the past 40 years’. The big data sets required for comparison are obtained from LAST.FM using its API. The detailed research paper would be available soon.

In the field of Hit Song Science, the intention is to detect, or rather predict, whether a song has the potential to become a commercial hit song, thus reaching the top of the record charts. Quite a few studies have already been done on this topic, some of which focused on extracting general acoustic and lyrical features from songs and then use standard classifiers to separate hits from non-hits. Another approach was to predict potential hit songs by using data mined from a music social network and the relationships between tracks, artists and albums . These studies produced some promising results, but there are still plentiful other approaches to follow. For instance, one could attempt a combination of the acoustic/lyrical features and social features.
The ability to detect potential hit songs would have an incredible impact on the music industry. There are online services, such as uPlaya, which claim to have already succeeded in Hit Song Science, promising that Hit Song Science™ provides immediate feedback on your song’s potential for commercial success and instant legitimization in the market for high-scoring music. This particular online service states that their rating system provides immediate feedback on the quality of music, its competitive edge in the music industry, and its reception among professionals and music lovers. However, even though uPlaya provides objective feedback on a song’s hit potential by comparing it against hit songs from the past, it does not predict whether a song will become a hit: it returns an evaluation based on certain ‘hit characteristics’.

There is still lots of room for improvement on hit song prediction, from which there is much to gain. First of all, it would greatly support record companies in pinpointing songs and artists that have the most potential of scoring a hit song. Furthermore, it could enhance music recommendation services in determining which songs and artists to recommend.

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Well, your parents might call something noise. Your children will call it Therapy.

From peaceful Beethoven music to electronic Dub-step. . Wow.. what a world we live in!

Music exists from as long as mankind does. It is older than agriculture, been longer than the wheel, discovered much before fire was and anthropologists say that it has been a cultural universal. Meaning that you can find it in any culture at any given point of time. But unfortunately, the taste of music is not the same. You might wonder how your parents enjoy with their vinyl collection, or how a bunch of ravers enjoy a genre called Hardstyle. Also now with access to online streaming audio accessibility and cloud storage facilities, music collection has become easier than ever. Music transcends from time to time. So, how would the music sound in Future?

New instruments are the birthplace of music revolution. Consider for example an electric guitar invented back in the 1930’s. Just imagine what the music for around 60-80 years after that would have sounded without its presence. Now, you can even find robot Guitars which can tune themselves.

Some recent discoveries in instruments would be silent drum, a drum with lasers used to change the pitch of the sound while playing, or a rubber keyed piano, which can bend its pitch perfectly. Some music composers take this digital mandate to a whole new level. One significant example would be Brian House, with his Quotidian record. he has managed to make music by tracking location data, i.e. making actual music based on the artists actual location. This could be augmented to any kind of data, take for example your home’s energy consumption data. This transcends music into a human itself.

Some other major changes predicted  in music sharing would be the end of MP3 era. ‘Lala’ has developed an application that will allow users to stream their entire music libraries on mobile phones directly in uncompressed wave files with data compression only in the data transmission.The company’s co-founder says it will replace MP3 s as the preferred format for digital music. Also Lala is already providing drastically lower pricing for music and more tunes for the people at the same time.

Search companies are listening users’ complaints that finding music on-line is a chore. Recently, Google debuted a much-hyped new search function that allows users to play and purchase audio files directly from a search results page, thanks to partnerships with several burgeoning music services .

The creation and production of music is also undergoing a revolution. uPlaya, a Web site launched earlier this summer uses an algorithm to determine the “hit potential” of any audio file a user uploads. By mathematically comparing a song’s features to past hits across multiple genres, the company claims to be able to predict its viability, allowing artists to adjust their new music to appeal to the widest possible audience.

So,in a précis, the advancements in technology are helping listening and producing music than ever before, we certainly have a great future in music.

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