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2014 popular songs on the radio

Individual songs might advance steadily toward No. They would come and go: rock or pop, soul or country, hip-hop or synth-pop. The format would hold them all. But the weekly show he introduced on July 4, — when the No. Echoing the broad mass appeal of Top 40 hits, the show took pains to exclude no one. Unlike countless disc jockeys, Mr. The dedications demonstrated how ordinary listeners were reacting to the songs, and how even the most forgettable ditty can move someone. With Mr. But in music, consensus can be a mixed blessing, rewarding the lowest common denominator rather than some higher unity. Reaching the pop charts implies that a song has surfaced from within its particular subculture — a place, a style, a genre — to draw new attention from outside.
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Billboard Year-End Hot chart for For detailed information on how Billboard compiled the charts, see the Wikipedia article " Billboard Hot ". Top s Songs.


We have a few guesses, but want to hear from you, too. The ode to party rocking is an undeniable force, still filling dance floors with trap beats and dance hooks months after it first dropped. Paired with a Daniels-directed video that manages to be both wildly inappropriate and yet completely appropriate for the song, at least , expect the song to be featured on party playlists through Labor Day.

About Billboard

In , a Stanford Ph. Their idea was to develop a service that could identify any song within a few seconds, using only a cellphone, even in a crowded bar or coffee shop. At first, Wang, who had studied audio analysis and was responsible for building the software, feared it might be an impossible task. No technology existed that could distinguish music from background noise, and cataloging songs note for note would require authorization from the labels. But then he made a breakthrough: rather than trying to capture whole songs, he built an algorithm that would create a unique acoustic fingerprint for each track. The trick, he discovered, was to turn a song into a piece of data. Shazam became available in In the days before smartphones, users would dial a number, play the song through their phones, and then wait for Shazam to send a text with the title and artist. Since then, it has been downloaded more than million times and used to identify some 30 million songs, making it one of the most popular apps in the world. It has also helped set off a revolution in the recording industry.

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The Billboard Hot is a chart that ranks the best-performing singles of the United States. Its data, published by Billboard magazine and compiled by Nielsen SoundScan , is based collectively on each single's weekly physical and digital sales, as well as airplay and streaming. Throughout a year, Billboard will publish an annual list of the most successful songs throughout that year on the Hot chart based on the information. For , the list was published on December 9, calculated with data from December 7, to November 29, This was the first time in eight years that a male artist topped the chart with a non-collaboration. From Wikipedia, the free encyclopedia. Prometheus Global Media. Retrieved December 10, Retrieved December 11, Billboard Year-End Hot singles.

In , a Stanford Ph. Their idea was to develop a service that could identify any song within a few seconds, using only a cellphone, even in a crowded bar or coffee shop. At first, Wang, who had studied audio analysis and was responsible for building the software, feared it might be an impossible task.

No technology existed that could distinguish music from background noise, and cataloging songs note for note would require authorization from the labels. But then he made a breakthrough: rather than trying to capture whole songs, he built an algorithm that would create a unique acoustic fingerprint for each track.

The trick, he discovered, was to turn a song into a piece of data. Shazam became available in In the days before smartphones, users would dial a number, play the song through their phones, and then wait for Shazam to send a text with the title and artist.

Since then, it has been downloaded more than million times and used to identify some 30 million songs, making it one of the most popular apps in the world. It has also helped set off a revolution in the recording industry. While most users think of Shazam as a handy tool for identifying unfamiliar songs, it offers music executives something far more valuable: an early-detection system for hits.

By studying 20 million searches every day, Shazam can identify which songs are catching on, and where, before just about anybody else. Titus is now a senior director at Google. The company has a team of people who update its vast music library with the newest recorded music—including self-produced songs—from all over the world, and artists can submit their work to Shazam. Take, for example, Lorde, the out-of-nowhere sensation of Shazam has become a favorite app of music agents around the country, and in February, the company announced that it would get into the music-making business itself, launching a new imprint under Warner Music Group for artists discovered through the app.

Shazam searches are just one of several new types of data guiding the pop-music business. Concert promoters study Spotify listens to route tours through towns with the most fans, and some artists look for patterns in Pandora streaming to figure out which songs to play at each stop on a tour. In fact, all of our searching, streaming, downloading, and sharing is being used to answer the question the music industry has been asking for a century: What do people want to hear next?

As a result, labels have gotten much better at understanding what we want to listen to. This is the one silver lining the music industry has found in the digital revolution, which has steadily cut into profits. Republic Records is the most data-driven major label in the music business even an executive at a rival label described Republic as the gold standard for using analytics in scouting and marketing , and Culbertson in particular has proved to be a star at the company. Pop music is a sentimental business, and predicting the next big thing has often meant being inside that crowded bar, watching a young band connect with the besotted, swaying throng.

But now that new artists are more likely to make a name for themselves on Twitter than in a Nashville club, Culbertson is finding that the chair in front of his computer might be the best seat in the house. New tools may soon further diminish the importance of actually hearing artists perform. Next Big Sound, a five-year-old music-analytics company based in New York, scours the Web for Spotify listens, Instagram mentions, and other traces of digital fandom to forecast breakouts.

It funnels half a million new acts through an algorithm to create a list of stars likely to break out within the next year. Last year, the company unveiled a customizable search tool called Find, which, for a six-figure annual subscription, helps scouts mine social media to spot artists who show signs of nascent stardom.

If, for example, you wanted to search for obscure bands with the fastest-growing followings on Twitter, Find could produce a list within seconds. To get a song on the radio in the first place, music labels confront a paradox: How do you prove that it will be a hit before anyone has heard it? In the past, labels sometimes pressured or outright bribed stations to promote their music.

Songs became hits because executives decided they should be hits. To persuade a major radio station to play a new song, labels have to connect all these dots. Nielsen Audio, another data firm that has partnered with the company, offers thousands of listeners cash or gift cards to wear devices called Portable People Meters that track which radio stations people are tuning in to.

To know when listeners are growing tired of a song, iHeartMedia conducts weekly surveys using a database of 1. Before a song debuts on a major chart—Top 40, urban, country, or alternative—HitPredictor plays key sections for its online database of listeners and rates their responses.

A similar revolution has occurred in the music charts. Take the Billboard Hot , which has counted down the top songs in America since For decades, Billboard had to rely on record-store owners and radio stations to report the most-bought and most-played songs.

The entire industry was biased toward churn: labels and stores wanted songs to enter and exit the charts quickly so they could keep selling new hits. In a groundbreaking study on the influence of song rankings, three researchers at Columbia University showed that popularity can be a self-fulfilling prophecy. The researchers sent participants to different music Web sites where they could listen to dozens of tracks and download their favorites.

Some sites displayed a ranking of the most-downloaded songs; others did not. Participants who saw rankings were more likely to listen to the most-popular tracks.

The researchers then wondered what would happen if they manipulated the rankings. In a follow-up experiment, some sites displayed the true download counts and others showed inverted rankings, where the least-popular song was listed in the No. The inverted rankings changed everything: previously ignored songs soared in popularity, and previously popular songs were ignored.

Simply believing, even wrongly, that a song was popular made participants more likely to download it. Billboard replaced its honor system with hard numbers in , basing its charts on point-of-sale data from cash registers. Another sea change came in the mids, when Billboard started tracking music streaming and downloads. But because the industry can now track what people are listening to, any song that catches on can become a hit.

Now that the Billboard rankings are a more accurate reflection of what people buy and play, songs stay on the charts much longer. The 10 songs that have spent the most time on the Hot were all released after , when Billboard started using point-of-sale data—and seven were released after the Hot began including digital sales, in Because the most-popular songs now stay on the charts for months, the relative value of a hit has exploded.

The top 1 percent of bands and solo artists now earn 77 percent of all revenue from recorded music, media researchers report. And even though the amount of digital music sold has surged, the 10 best-selling tracks command 82 percent more of the market than they did a decade ago.

Radio stations, meanwhile, are pushing the boundaries of repetitiveness to new levels. According to a subsidiary of iHeartMedia, Top 40 stations last year played the 10 biggest songs almost twice as much as they did a decade ago. And not only are we hearing the same hits with greater frequency, but the hits themselves sound increasingly alike. In , the Spanish National Research Council released a report that delighted music cranks around the world.

Pop, it seemed, was growing increasingly bland, loud, and predictable, recycling the same few chord progressions over and over. The problem is not our pop stars. Our brains are wired to prefer melodies we already know. In psychology, this idea is known as fluency: when a piece of information is consumed fluently, it neatly slides into our patterns of expectation, filling us with satisfaction and confidence.

You want to eat comfort food. I think this maps onto a lot of media consumption. You want the old and familiar. It would be too simplistic to say that music is racing in a single direction—toward dumber, louder, and more-repetitive pop. Now that labels recognize how popular hip-hop and country really are, they have created innovative new sounds by blending those genres with traditional pop. It was utterly strange and, for a while, ubiquitous.

Greta Hsu, an associate professor at the University of California at Davis, who has done research on genre-blending in Hollywood, told me that although mixing categories is risky, hybrids can become standout successes, because they appeal to multiple audiences as being somehow both fresh and familiar. Music fans can also find comfort in the fact that data have not taken over the songwriting process.

A Harvard study found that music performed by robotic drummers and other machines often strikes our ears as being too precise. Hennig discovered that when experienced musicians play together, they not only make mistakes, they also build off these small variations to keep a live song from sounding pat. The Internet can connect us to an astonishing amount of music—some of it derivative, but much of it wildly experimental, even brilliant.

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