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The Echo Nest at Music and Bits, October 21 2009

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    The Echo Nest at Music and Bits, October 21 2009 - Presentation Transcript

    1. Thursday, October 22, 2009
    2. I am losing my voice. I am sorry. I am normally louder than this. I also added text to the pictures. Thursday, October 22, 2009
    3. A Short (Personal) History of Computers Listening to Music 1999-2009 Thursday, October 22, 2009
    4. I was a musician for a while. Electronic music. “Intelligent dance music” (worst genre name ever) Thursday, October 22, 2009
    5. Thursday, October 22, 2009
    6. “Fish / Cut bait” Handheld-music (1998-2001) I made my own software to make music Thursday, October 22, 2009
    7. “Fish / Cut bait” Handheld-music (1998-2001) Did it make me a better musician? Definitely not. Thursday, October 22, 2009
    8. It was 1999. Lots of stuff was happening. Thursday, October 22, 2009
    9. I learned about music from reading web sites. Forums, mailing lists. Thursday, October 22, 2009
    10. Thursday, October 22, 2009
    11. You could now download a song faster than real time. I figured things would change quick. Thursday, October 22, 2009
    12. So I went to grad school. I studied information retrieval, language processing. Thursday, October 22, 2009
    13. Columbia University, NYC MIT Media Lab finishing my dissertation Thursday, October 22, 2009
    14. People were starting to apply IR techniques to music. Audio files are treated like text. FFT frames became words Songs became “documents” Thursday, October 22, 2009
    15. Thursday, October 22, 2009
    16. There’s a problem with that. Just because you can convert an mp3 to #s doesn’t mean you understand it. Thursday, October 22, 2009
    17. “Music IR” was born. The applications are varied, but most have nothing to do with music. Thursday, October 22, 2009
    18. Retrieving Music by Rhythmic Similarity 1.5 110 bpm 112 bpm 1 squared Euclidean distance 114 bpm 120 bpm 122 bpm 116 bpm 124 bpm 0.5 126 bpm 128 bpm 130 bpm 0 130 128 126 124 122 120 118 116 114 112 110 Tempo (bpm) Figure 5. Euclidean Distance vs. Tempo Thursday, October 22, 2009
    19. The worst offender: “Genre Identification” Countless PhDs on this useless task. Trying to teach a computer a marketing construct. Thursday, October 22, 2009
    20. Show of hands: Is Bjork “electronic, pop, jazz”? Thursday, October 22, 2009
    21. At MIT I convinced someone to buy lots of computers Thursday, October 22, 2009
    22. Thursday, October 22, 2009
    23. And tried to figure out how to get music into music analysis Thursday, October 22, 2009
    24. Simple things like detecting holiday music is very hard. Thursday, October 22, 2009
    25. I decided if I could get a computer to make holiday music, We could claim we understand it. Thursday, October 22, 2009
    26. This is automatically generated holiday music Music Acquisition (2001-) based on listening to 1,000 Christmas songs Thursday, October 22, 2009
    27. It should be a funny joke that you can run statistics of millions of things and “understand it.” Thursday, October 22, 2009
    28. Thursday, October 22, 2009
    29. I built Eigenradio in 2003 to show people What computers hear when they hear music Thursday, October 22, 2009
    30. Thursday, October 22, 2009
    31. There’s obviously so much more to music than the audio signal and that other stuff is probably more important Thursday, October 22, 2009
    32. My brother makes music with sine waves and nothing else and gets a 9.7 on Pitchfork. This is fascinating! Thursday, October 22, 2009
    33. My brother makes music with sine waves and nothing else and gets a 9.7 on Pitchfork. This is fascinating! Were the sine waves that good? Thursday, October 22, 2009
    34. Review Regression Thursday, October 22, 2009
    35. It turns out if you understand language and audio at the same time you start learning a lot more. Thursday, October 22, 2009
    36. Here we predict ratings on All Music Guide and Pitchfork By listening to the audio and reading about the artist. Thursday, October 22, 2009
    37. Audio alone was terrible Text alone was better than audio Both together were the best. Thursday, October 22, 2009
    38. 100 100 12 10 80 80 Pitchfork Ratings Pitchfork Ratings 10 8 60 8 60 6 40 6 40 4 4 20 20 2 2 2 4 6 8 2 4 6 8 AMG Ratings Randomly selected AMG Ratings 100 .147 120 .127 6 Audio−derived Ratings Audio−derived Ratings 8 80 6 [.080] 100 80 60 [.082] 5 4 60 3 4 40 40 2 2 20 20 1 2 4 6 8 20 40 60 80 100 AMG Ratings Pitchfork Ratings Thursday, October 22, 2009
    39. I became interested in more ridiculous questions: “Can we find the saddest song in the world?” Thursday, October 22, 2009
    40. Thursday, October 22, 2009
    41. So I started a company in 2005 with my co-founder Tristan, also at the Lab. Thursday, October 22, 2009
    42. Tristan is a DSP “machine listening” expert and I handled the text side Thursday, October 22, 2009
    43. MAGIC Thursday, October 22, 2009
    44. Why does the Echo Nest exist? Thursday, October 22, 2009
    45. The best music experience is still very manual. I am still reading about music, not using a recommender. Thursday, October 22, 2009
    46. Thursday, October 22, 2009
    47. Thursday, October 22, 2009
    48. & the act of listening to music is easier than ever Thursday, October 22, 2009
    49. Thursday, October 22, 2009
    50. But data is hard. Most designers make very bad decisions because their tools are inefficient. Thursday, October 22, 2009
    51. Collaborative filtering (X who did Y also did Z) is so easy to make; but it’s also so terrible. Thursday, October 22, 2009
    52. Collaborative filtering (X who did Y also did Z) is so easy to make; but it’s also so terrible. The SQL join is destroying music. Thursday, October 22, 2009
    53. Thursday, October 22, 2009
    54. Thursday, October 22, 2009
    55. Thursday, October 22, 2009
    56. Thursday, October 22, 2009
    57. In 2005 we modeled the worst case scenario: In which collaborative filtering was the only way for an artist to get noticed. The popular ones would eat the unknown ones alive. 3 sets of 3 artists each remained. Thursday, October 22, 2009
    58. Set A Set B Set C Britney Spears Alice in Chains Chris Isaak Backstreet Boys Korn Bob Dylan Cristina Aguilera Faith no More Crowded House Thursday, October 22, 2009
    59. So the Echo Nest gives everyone great data. They can decide on their own how to show it. Thursday, October 22, 2009
    60. The Echo Nest 2005 Somerville, MA USA 2 people 2 computers Lots of ideas 1m documents 10,000 artists 100,000 songs 0 public facing sites Thursday, October 22, 2009
    61. The Echo Nest 2009 Somerville, MA USA 20 people 200 computers Lots of products 5bn documents 1,000,000 artists many millions of songs 0 public facing sites Thursday, October 22, 2009
    62. What We Do Thursday, October 22, 2009
    63. “Know everything about music and listeners.” Thursday, October 22, 2009
    64. “Know everything about music and listeners.” “Give (and sell) great data to everyone.” Thursday, October 22, 2009
    65. “Know everything about music and listeners.” “Give (and sell) great data to everyone.” “Do it automatically with no bias, on everything.” Thursday, October 22, 2009
    66. Customers Crawling Code NLP DSP Machine Learning Thursday, October 22, 2009
    67. Artist Data Song Data Listener Data • Tag Clouds • Similar Songs • demographics • Similar Artists • Tempo - age, gender, location • Analytics • Key • psychographics Familiarity Mode - preferences, lifestyle • • • music preference • Hotttnesss • Time Signature Blogs Beats • listening patterns • • News Downbeats • tastemaker profiling • • - writers, bloggers • Reviews • Segments • Audio • Timbre • Video • Pitch • Profile Sites • Loudness • Misspellings • Sections • Aliases Thursday, October 22, 2009
    68. We have a lot of data and we have a lot of products. We sell mostly to social networks, labels; video games; PR firms; musicians Thursday, October 22, 2009
    69. Artist metrics Acoustic Similarity analysis Feeds Remix Metadata Search / Tags Predictive Recommendation analytics Thursday, October 22, 2009
    70. The reason we are special is 2 things: Scale and Platform Thursday, October 22, 2009
    71. Our scale is limitless. We have hundreds of computers We always do our computation on everything. We can learn about new music very quickly. Thursday, October 22, 2009
    72. Scale All Music Guide Pandora The Echo Nest known artists 280,000 80,000 1,000,000 years to get there 18 8 1 time to understand 1 week 1 day <1 minute one album cost to understand $400 $40 $0.001 one album Thursday, October 22, 2009
    73. Our platform is huge. We have thousands of “free” developers using our API Our customers use the same platform So do we. Thursday, October 22, 2009
    74. Platform Thursday, October 22, 2009
    75. We sell two main products: Fanalytics is a predictive analytics toolset for artists The Knowledge is a dynamic metadata service (recommendation, feeds, data) for web sites Thursday, October 22, 2009
    76. Fanalytics lets artists and labels get a view into the world of online music We recommend blogs for artists We show predicted analytics on activity Thursday, October 22, 2009
    77. Predictive analytics Artist metrics Thursday, October 22, 2009
    78. We also maintain a popular open source remixing community and code base so people can make awesome free mashups, remixes, web sites using our tech Not much of a business but we love it. Thursday, October 22, 2009
    79. Remix Thursday, October 22, 2009
    80. “DonkDJ.com” was made using Remix It automatically “donks” (ask someone what this means) any song you upload Thursday, October 22, 2009
    81. Thursday, October 22, 2009
    82. Morecowbell.dj adds cowbell to any song This Is My Jam was a pre-Muxtape (by one day) mixtape sharing site that only let you use 30s samples and made a total mess of the output. Like I said, not much of a business. Thursday, October 22, 2009
    83. Thursday, October 22, 2009
    84. Thursday, October 22, 2009
    85. We also have artists using Remix -- our data is now powering some next generation electronic music Thursday, October 22, 2009
    86. I’ve always wanted to hear Michael Jackson trying to sing Amerie’s “One Thing” automatically by comparing timbre, pitch and loudness distances. -B.L. Thursday, October 22, 2009
    87. James Brown... FOREVER. Thursday, October 22, 2009
    88. Remix also works on video Thursday, October 22, 2009
    89. Let’s hear Daft Punk’s “Revolution 909” played by a fight scene from Undefeatable! -Y.A. Thursday, October 22, 2009
    90. Our analysis data powers a lot of visualizers and video games (rhythm games on your own MP3s) Thursday, October 22, 2009
    91. Acoustic analysis Thursday, October 22, 2009
    92. The Knowledge is a much better music data service Customers can subscribe to constantly-updated similarity, metadata, feeds, recommendations, etc Thursday, October 22, 2009
    93. Our similarity and recommendation data is some of the best, because we use so many sources and we know about all artists even if they are tiny Thursday, October 22, 2009
    94. Similarity Feeds Thursday, October 22, 2009
    95. Since our similarity is based on so many features: popularity, audio analysis, text analysis, structured metadata, influences, ... Thursday, October 22, 2009
    96. Since our similarity is based on so many features: popularity, audio analysis, text analysis, structured metadata, influences, ... We provide our customers with the knobs and let them decide what is important for the task. Thursday, October 22, 2009
    97. Since our similarity is based on so many features: popularity, audio analysis, text analysis, structured metadata, influences, ... We provide our customers with the knobs and let them decide what is important for the task. We do not give a “single answer.” There is no single answer. Thursday, October 22, 2009
    98. Similarity Thursday, October 22, 2009
    99. We can build paths between artists on any vector Thursday, October 22, 2009
    100. Acoustic Similarity analysis Search / Tags Thursday, October 22, 2009
    101. Our future: Thursday, October 22, 2009
    102. 1. Listener analytics Thursday, October 22, 2009
    103. We’ve been running large scale data mining on millions of listeners to help with analytics, for example a gender predictor based on your music taste Thursday, October 22, 2009
    104. Here’s the basis vectors; strongest correlators of gender: Thursday, October 22, 2009
    105. Male Female Pet Shop Boys Eternal Fort Minor Metro Station Justice Gackt Mike Oldfield Paolo Nutini U2 London after Midnight Thursday, October 22, 2009
    106. 2. More musicians to use our remix tools Thursday, October 22, 2009
    107. (I’ve noticed the better you are with computers, the worse your music is. This may just be me) Thursday, October 22, 2009
    108. 100% 75% Music goodness 50% 25% 0% nothing not much a little somewhat pretty good expert dork prime Computers know-how Thursday, October 22, 2009
    109. 3. Search anything APIs Thursday, October 22, 2009
    110. We will soon make all of our acoustic data available for searching and browsing (right now it has to be your content): “Find me a drum hit in this collection that sounds like the break in ‘Single Ladies’” Thursday, October 22, 2009
    111. Thursday, October 22, 2009
    112. Combined with Remix this will allow anyone to compose music that uses all music in the world Thursday, October 22, 2009
    113. >> from echonest import search >> segments = search.query(“voice”, soundsLike=”bjork”, pitch=”F#”) >> len(segments) 65706 >> new_song = random.shuffle(segments).write(“bjork2009.mp3”) Thursday, October 22, 2009
    114. To wrap up: 1. Don’t trust computers Thursday, October 22, 2009
    115. To wrap up: 1. Don’t trust computers 2. But trust us, really Thursday, October 22, 2009
    116. To wrap up: 1. Don’t trust computers 2. But trust us, really 3. Sorry I can’t speak very well Thursday, October 22, 2009
    117. Thursday, October 22, 2009

    + Brian WhitmanBrian Whitman, 1 month ago

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