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Most Obscure Hit Songs, Adjusted for Time
 1 Roy Ingraham Orch: Chant of the Jungle, 1930 2 Hilo Hawaiian Orch: When It’s Springtime in the Rockies, 1930 3 Horace Wright: My Own Iona, 1917 4 Marguerite Farrell: If I Knock the ‘L’ Out of Kelly (It Would Still be Kelly to Me), 1916 5 Olive Kline: Hello, Frisco!, 1915 6 Orpheus Quartet: Turn Back the Universe and Give Me Yester Day, 1916 7 Horace Heidt Orch: Ti-Pi-Tin, 1938 8 Clay Aiken: This Is The Night, 2003 9 Mina Hickman: Come Down, Ma Evening Star, 1903 10 Don Bestor Orch: Forty-Second Street, 1933

Details: The Songs That Time Forgot | Very Small Array

    Most Obscure Hit Songs, Adjusted for Time


    1 Roy Ingraham Orch: Chant of the Jungle, 1930
    2 Hilo Hawaiian Orch: When It’s Springtime in the Rockies, 1930
    3 Horace Wright: My Own Iona, 1917
    4 Marguerite Farrell: If I Knock the ‘L’ Out of Kelly (It Would Still be Kelly to Me), 1916
    5 Olive Kline: Hello, Frisco!, 1915
    6 Orpheus Quartet: Turn Back the Universe and Give Me Yester Day, 1916
    7 Horace Heidt Orch: Ti-Pi-Tin, 1938
    8 Clay Aiken: This Is The Night, 2003
    9 Mina Hickman: Come Down, Ma Evening Star, 1903
    10 Don Bestor Orch: Forty-Second Street, 1933

    Details: The Songs That Time Forgot | Very Small Array

    — 2 months ago
    #Popularity  #Charts  #Data 
    
It began with an argument. Tristan Jehan and Brian Whitman met as Ph.D. candidates at MIT’s Media Lab. Both were amateur musicians passionate about the ways technology might recommend songs based on a listener’s tastes. Both were convinced that “collaborative filtering,” a trendy means of achieving that goal, was woefully inadequate. Their disagreement? Jehan’s research focused on teaching computers to capture the sonic elements of music, while Whitman’s studied the cultural and social components. In combining the two approaches they created the Echo Nest, one of the most important digital music companies few have heard about. 

The rest: The big music brain that knows what you like - Fortune Tech

    It began with an argument. Tristan Jehan and Brian Whitman met as Ph.D. candidates at MIT’s Media Lab. Both were amateur musicians passionate about the ways technology might recommend songs based on a listener’s tastes. Both were convinced that “collaborative filtering,” a trendy means of achieving that goal, was woefully inadequate. Their disagreement? Jehan’s research focused on teaching computers to capture the sonic elements of music, while Whitman’s studied the cultural and social components. In combining the two approaches they created the Echo Nest, one of the most important digital music companies few have heard about. 

    The rest: The big music brain that knows what you like - Fortune Tech

    — 1 year ago with 2 notes
    #Data  #Business 
    
In this article, we’ll look at the statistics gathered from 1300 choruses, verses, etc. of popular songs to discover the answer to a few basic questions. First we’ll look at the relative popularity of different chords based on the frequency that they appear in the chord progressions of popular music. Then we’ll begin to look at the relationship that different chords have with one another. For example, if a chord is found in a song, what can we say about the probability for what the next chord will be that comes after it?


via I analyzed the chords of 1300 popular songs for patterns. This is what I found. | Blog – Hooktheory

    In this article, we’ll look at the statistics gathered from 1300 choruses, verses, etc. of popular songs to discover the answer to a few basic questions. First we’ll look at the relative popularity of different chords based on the frequency that they appear in the chord progressions of popular music. Then we’ll begin to look at the relationship that different chords have with one another. For example, if a chord is found in a song, what can we say about the probability for what the next chord will be that comes after it?

    via I analyzed the chords of 1300 popular songs for patterns. This is what I found. | Blog – Hooktheory

    — 2 years ago with 1 note
    #Data 
    
It took a month, but word is spreading about a new chart from Flurry Analytics showing the increases in time spent by users in various kinds of apps from October 2011 to March 2012.
The biggest gain: photo and video, “enabled by a confluence of underlying technology like built-in HD video cameras, hardy on-device processors, increased network bandwidth, cloud storage and user-friendly applications like Viddy and SocialCam.”
The second-biggest gain in time per active user: music apps, which comes as no surprise to us.

(via People Are Spending Way More Time on Music Apps | Evolver.fm)

    It took a month, but word is spreading about a new chart from Flurry Analytics showing the increases in time spent by users in various kinds of apps from October 2011 to March 2012.

    The biggest gain: photo and video, “enabled by a confluence of underlying technology like built-in HD video cameras, hardy on-device processors, increased network bandwidth, cloud storage and user-friendly applications like Viddy and SocialCam.”

    The second-biggest gain in time per active user: music apps, which comes as no surprise to us.

    (via People Are Spending Way More Time on Music Apps | Evolver.fm)

    — 2 years ago
    #Apps  #Data 
    
The peak in usage of the word ‘fuck’ in song titles occurs in 2006 with 650 songs. Since then, peak usage has dropped off substantially, 2011 saw about the same ‘fuck’ frequency as 1999.

More data: Is music getting more profane? « Music Machinery

    The peak in usage of the word ‘fuck’ in song titles occurs in 2006 with 650 songs. Since then, peak usage has dropped off substantially, 2011 saw about the same ‘fuck’ frequency as 1999.

    More data: Is music getting more profane? « Music Machinery

    — 2 years ago with 3 notes
    #Data  #Profanity  #Lyrics  #Titles 
    


On Quora, someone asked the question: Are band names getting longer? To answer that question I used the Echo Nest API to get the top 500 artists for each five year window starting in 1950  and calculated the average name length for each of the artists.
The answer to the question as to whether artist names have been getting longer is a surprising No.



(via Have artist names been getting longer? « Music Machinery)

    On Quora, someone asked the question: Are band names getting longer? To answer that question I used the Echo Nest API to get the top 500 artists for each five year window starting in 1950 and calculated the average name length for each of the artists.

    The answer to the question as to whether artist names have been getting longer is a surprising No.

    (via Have artist names been getting longer? « Music Machinery)

    — 2 years ago with 1 note
    #Data  #Band Names 
    CHART OF THE DAY: The Death Of Music Industry Is Much Worse Than We Thought

Earlier this week we published a chart showing the utter collapse of music sales during the digital era.
One reader took issue with our chart, saying it didn’t capture the  full collapse of the business because it wasn’t inflation adjusted. He produced the chart below saying, music sales are down 64% from their peak, not 45% as the other chart suggested.

    CHART OF THE DAY: The Death Of Music Industry Is Much Worse Than We Thought

    Earlier this week we published a chart showing the utter collapse of music sales during the digital era.

    One reader took issue with our chart, saying it didn’t capture the full collapse of the business because it wasn’t inflation adjusted. He produced the chart below saying, music sales are down 64% from their peak, not 45% as the other chart suggested.

    — 3 years ago
    #Music Business  #Data 
    Girl Talk's Latest Mashup Masterpiece Deconstructed →

    Here’s a stab by Fast Company and infographic artist Tiffany Farrant at mapping the samples on Girl Talk’s latest, “All Day.”

    — 3 years ago
    #Sampling  #Graphics  #Data  #Visuals 
    "Amassing more songs and managing the metadata and organizing the music library all begin to cannibalize the pleasure of the music itself. Or rather, these data-driven pleasures mediate our experience of music in a different way from what we knew before mp3s. The music becomes more like information, requiring less of a sensual surrender. Girl Talk seems emblematic of music created to suit this new aesthetic; classifying the samples becomes inseparable from the pleasures of listening to it."
    — 3 years ago
    #Listening  #Data 
    "Nifty new visualization at Last.fm that shows the time of day when you listen to music."
Last.FM’s Listening clock « Music Machinery

    "Nifty new visualization at Last.fm that shows the time of day when you listen to music."

    Last.FM’s Listening clock « Music Machinery

    — 4 years ago
    #Data 
    "Artist Nathalie Miebach extracts weather data from different cities and translates it into functional musical scores which she then uses to create sculptures." 
(Pics) Meteorological Data Turned Into Sculptural Music Scores - PSFK

    "Artist Nathalie Miebach extracts weather data from different cities and translates it into functional musical scores which she then uses to create sculptures." 

    (Pics) Meteorological Data Turned Into Sculptural Music Scores - PSFK

    — 4 years ago
    #Data  #Objects 
    A look at the distribution of the Top 100 iPhone/Touch apps. Music is in a respectable slot, above healthcare/fitness, finance, even social networking, though well below games, books, and utilities. Overall, this makes for a healthy outlook for mainstream adoption of interactive sound. —  Disquiet » Image of the Week: The Mainstreaming of Interactive

    A look at the distribution of the Top 100 iPhone/Touch apps. Music is in a respectable slot, above healthcare/fitness, finance, even social networking, though well below games, books, and utilities. Overall, this makes for a healthy outlook for mainstream adoption of interactive sound. — Disquiet » Image of the Week: The Mainstreaming of Interactive

    — 4 years ago
    #Interacting,  #Apps  #Data