The Echo Nest at Music and Bits, October 21 2009
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Transcript of The Echo Nest at Music and Bits, October 21 2009
Thursday, October 22, 2009
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
A Short (Personal) History ofComputers Listening to Music
1999-2009
Thursday, October 22, 2009
I was a musician for a while.Electronic music.
“Intelligent dance music”(worst genre name ever)
Thursday, October 22, 2009
Thursday, October 22, 2009
“Fish / Cut bait”
Handheld-music (1998-2001)I made my own software to make music
Thursday, October 22, 2009
“Fish / Cut bait”
Handheld-music (1998-2001)Did it make me a better musician? Definitely not.
Thursday, October 22, 2009
It was 1999. Lots of stuff was happening.
Thursday, October 22, 2009
I learned about music from reading web sites.Forums, mailing lists.
Thursday, October 22, 2009
Thursday, October 22, 2009
You could now download a song faster than real time.I figured things would change quick.
Thursday, October 22, 2009
So I went to grad school.I studied information retrieval, language processing.
Thursday, October 22, 2009
Columbia University, NYC
MIT Media Labfinishing my dissertation
Thursday, October 22, 2009
People were starting to apply IR techniques to music.Audio files are treated like text.
FFT frames became wordsSongs became “documents”
Thursday, October 22, 2009
Thursday, October 22, 2009
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
“Music IR” was born.The applications are varied, but most
have nothing to do with music.
Thursday, October 22, 2009
Retrieving Music by Rhythmic Similarity
cal excerpt. (The effect of varying the truncated regions was notexamined, and it is not unlikely that other values may result in bet-ter retrieval performance.)
4.1.1 Euclidean DistanceThree different distance measures were used. The first wasstraightforward squared Euclidean distance measure, or the sum ofthe squares of the element-by-element differences between the val-ues, as used in Experiment 1. For evaluation, each excerpt wasused as a query. Each of the 15 corpus documents was then rankedby similarity to each of the 15 queries using the squared Euclideandistance. (For the purposes of ranking, the squared distance servesas well as the distance, as the square root function is monotonic.)Each query had 2 relevant documents in the corpus, so this waschosen as the cutoff point for measuring retrieval precision. Thusthere were 30 relevant documents for this query set. For eachquery, documents were ranked by increasing Euclidean distancefrom the query. Using this measure, 24 of the 30 possible docu-ments were relevant (i.e. from the same relevance class), giving aretrieval precision of 80%. (More sophisticated analyses such asROC curves, are probably not warranted due to the small corpussize.)
4.1.2 Cosine DistanceThe second measure used is a cosine metric, similar to thatdescribed in the previous section. This distance measure may bepreferable because it is less sensitive to the actual magnitudes ofthe vectors involved. This measure proved to perform significantlybetter than the Euclidean distance. Using this measure, 29 of the 30
documents retrieved were relevant, giving a retrieval precision of96.7% at this cutoff.
4.1.3 Fourier Beat Spectral CoefficientsThe final distance measure is based on the Fourier coefficients ofthe beat spectrum, because they can represent the rough spectralshape with many fewer parameters. A more compact representa-tion is valuable for a number of reasons: for example, fewer ele-ments speeds distance comparisons and also reduces the amount ofdata that must be stored to represent each file. To this effect, thefast Fourier transform was computed for each beat spectral vector.The log of the magnitude was then determined, and the mean sub-tracted from each coefficient. Because high “frequencies” in thebeat spectra are not rhythmically significant, the transform resultswere truncated to the 25 lowest coefficients. Additionally thezeroth coefficient was ignored, as the DC component is insignifi-cant for zero-mean data. The cosine distance metric was computedfor the 24 zero-mean Fourier coefficients, which served as the finaldistance metric. Experimentally, this measure performed identi-cally to the cosine metric, yielding 29 of 30 relevant documents or96.7% precision. Note that this performance was achieved using anorder of magnitude fewer parameters.Though this corpus is admittedly very small, there is no reason thatthe methods presented here could not be scaled to thousands oreven millions of works. Computing the beast spectrum is computa-tionally quite reasonable and can be done several times faster thanreal time, and even more rapidly if spectral parameters can bederived directly from MP3 compressed data as in [12] and [13].Additionally, well-known database organization methods can dra-
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Tempo (bpm)
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110 130 120 122 124 126 128 118 116 114 112
Figure 5. Euclidean Distance vs. Tempo
110 bpm
112 bpm
114 bpm
116 bpm
120 bpm
122 bpm
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126 bpm
128 bpm
130 bpm
Thursday, October 22, 2009
The worst offender: “Genre Identification”Countless PhDs on this useless task.
Trying to teach a computer a marketing construct.
Thursday, October 22, 2009
Show of hands:
Is Bjork “electronic, pop, jazz”?
Thursday, October 22, 2009
At MIT I convinced someone to buy lots of computers
Thursday, October 22, 2009
Thursday, October 22, 2009
And tried to figure out how to get musicinto music analysis
Thursday, October 22, 2009
Simple things like detecting holiday musicis very hard.
Thursday, October 22, 2009
I decided if I could get a computer to makeholiday music,
We could claim we understand it.
Thursday, October 22, 2009
Music Acquisition (2001-)This is automatically generated holiday music
based on listening to 1,000 Christmas songs
Thursday, October 22, 2009
It should be a funny joke that you can run statistics of millions of things
and “understand it.”
Thursday, October 22, 2009
Thursday, October 22, 2009
I built Eigenradio in 2003 to show peopleWhat computers hear when they hear music
Thursday, October 22, 2009
Thursday, October 22, 2009
There’s obviously so much more to musicthan the audio signaland that other stuff
is probably more important
Thursday, October 22, 2009
My brother makes music with sine wavesand nothing else
and gets a 9.7 on Pitchfork.This is fascinating!
Thursday, October 22, 2009
My brother makes music with sine wavesand nothing else
and gets a 9.7 on Pitchfork.This is fascinating!
Were the sine waves that good?
Thursday, October 22, 2009
Review Regression (2004)Thursday, October 22, 2009
It turns out if you understand languageand audio
at the same time you start learning a lot more.
Thursday, October 22, 2009
Here we predict ratings on All Music Guideand Pitchfork
By listening to the audio and reading about the artist.
Thursday, October 22, 2009
Audio alone was terribleText alone was better than audio
Both together were the best.
Thursday, October 22, 2009
AMG Ratings
Pitc
hfor
k R
atin
gs
2 4 6 8
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Randomly selected AMG Ratings
Pitc
hfor
k R
atin
gs
2 4 6 8
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AMG Ratings
Audi
o−de
rived
Rat
ings
2 4 6 8
2
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Pitchfork Ratings
Audi
o−de
rived
Rat
ings
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120.147[.080]
.127[.082]
Thursday, October 22, 2009
I became interested in more ridiculous questions:“Can we find the saddest song in the world?”
Thursday, October 22, 2009
Thursday, October 22, 2009
So I started a company in 2005with my co-founder Tristan, also at the Lab.
Thursday, October 22, 2009
Tristan is a DSP “machine listening” expertand I handled the text side
Thursday, October 22, 2009
MAGIC
Thursday, October 22, 2009
Why does the Echo Nest exist?
Thursday, October 22, 2009
The best music experience is still very manual.I am still reading about music, not using a recommender.
Thursday, October 22, 2009
Thursday, October 22, 2009
Thursday, October 22, 2009
& the act of listening to music is easier than ever
Thursday, October 22, 2009
Thursday, October 22, 2009
But data is hard.Most designers make very bad decisions
because their tools are inefficient.
Thursday, October 22, 2009
Collaborative filtering (X who did Y also did Z)is so easy to make; but it’s also so terrible.
Thursday, October 22, 2009
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
Thursday, October 22, 2009
Thursday, October 22, 2009
Thursday, October 22, 2009
Thursday, October 22, 2009
In 2005 we modeled the worst case scenario:
In which collaborative filtering was the only wayfor 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
Set ABritney Spears
Backstreet BoysCristina Aguilera
Set BAlice in Chains
KornFaith no More
Set CChris IsaakBob Dylan
Crowded House
Thursday, October 22, 2009
So the Echo Nest gives everyone great data.They can decide on their own how to show it.
Thursday, October 22, 2009
The Echo Nest 2005
Somerville, MA USA2 people2 computersLots of ideas1m documents10,000 artists100,000 songs0 public facing sites
Thursday, October 22, 2009
The Echo Nest 2009
Somerville, MA USA20 people200 computersLots of products5bn documents1,000,000 artistsmany millions of songs0 public facing sites
Thursday, October 22, 2009
What We Do
Thursday, October 22, 2009
“Know everything about music and listeners.”
Thursday, October 22, 2009
“Know everything about music and listeners.”“Give (and sell) great data to everyone.”
Thursday, October 22, 2009
“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
CodeCustomers Crawling
Machine LearningNLP DSP
Thursday, October 22, 2009
• Similar Songs• Tempo• Key• Mode• Time Signature• Beats• Downbeats• Segments• Timbre• Pitch• Loudness• Sections
• demographics - age, gender, location• psychographics - preferences, lifestyle• music preference • listening patterns• tastemaker profiling- writers, bloggers
Artist Data Song Data Listener Data• Tag Clouds• Similar Artists• Analytics• Familiarity • Hotttnesss• Blogs• News • Reviews• Audio• Video • Profile Sites• Misspellings• Aliases
Thursday, October 22, 2009
We have a lot of data andwe have a lot of products.
We sell mostly to social networks, labels;video games; PR firms; musicians
Thursday, October 22, 2009
Similarity
Acousticanalysis
Artist metrics
FeedsRemix
Recommendation
Search / TagsMetadata
Predictive analytics
Thursday, October 22, 2009
The reason we are special is 2 things:
Scale and Platform
Thursday, October 22, 2009
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
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 one album 1 week 1 day <1 minute
cost to understand one album $400 $40 $0.001
Scale
Thursday, October 22, 2009
Our platform is huge. We have thousands of “free” developers using our API
Our customers use the same platformSo do we.
Thursday, October 22, 2009
Platform
Thursday, October 22, 2009
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
Fanalytics lets artists and labels get a viewinto the world of online music
We recommend blogs for artistsWe show predicted analytics on activity
Thursday, October 22, 2009
Predictive analytics
Artist metrics
Thursday, October 22, 2009
We also maintain a popular open sourceremixing community and code baseso 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
Remix
Thursday, October 22, 2009
“DonkDJ.com” was made using RemixIt automatically “donks” (ask someone what this means)
any song you upload
Thursday, October 22, 2009
Thursday, October 22, 2009
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
Thursday, October 22, 2009
Thursday, October 22, 2009
We also have artists using Remix-- our data is now powering some next generation
electronic music
Thursday, October 22, 2009
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
James Brown... FOREVER.
Thursday, October 22, 2009
Remix also works on video
Thursday, October 22, 2009
Let’s hear Daft Punk’s “Revolution 909” played by a fight scene from Undefeatable!
-Y.A.
Thursday, October 22, 2009
Our analysis data powers a lot of visualizers andvideo games (rhythm games on your own MP3s)
Thursday, October 22, 2009
Acousticanalysis
Thursday, October 22, 2009
The Knowledge is a much better music data serviceCustomers can subscribe to constantly-updatedsimilarity, metadata, feeds, recommendations, etc
Thursday, October 22, 2009
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
Similarity
Feeds
Thursday, October 22, 2009
Since our similarity is based on so many features:popularity, audio analysis, text analysis,
structured metadata, influences, ...
Thursday, October 22, 2009
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
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
Similarity
Thursday, October 22, 2009
We can build paths between artists on any vector
Thursday, October 22, 2009
Similarity
Acousticanalysis
Search / Tags
Thursday, October 22, 2009
Our future:
Thursday, October 22, 2009
1. Listener analytics
Thursday, October 22, 2009
We’ve been running large scale data miningon millions of listeners to help with analytics,
for example a gender predictor based on your music taste
Thursday, October 22, 2009
Here’s the basis vectors; strongest correlators of gender:
Thursday, October 22, 2009
Male Female
Pet Shop Boys Eternal
Fort Minor Metro Station
Justice Gackt
Mike Oldfield Paolo Nutini
U2 London after Midnight
Thursday, October 22, 2009
2. More musicians to use our remix tools
Thursday, October 22, 2009
(I’ve noticed the better you are with computers,the worse your music is. This may just be me)
Thursday, October 22, 2009
0%
25%
50%
75%
100%
nothing not much a little somewhat pretty good expert dork prime
Mus
ic g
oodn
ess
Computers know-how
Thursday, October 22, 2009
3. Search anything APIs
Thursday, October 22, 2009
We will soon make all of our acoustic dataavailable for searching and browsing
(right now it has to be your content):
“Find me a drum hit in this collectionthat sounds like the break in ‘Single Ladies’”
Thursday, October 22, 2009
Thursday, October 22, 2009
Combined with Remix this will allow anyoneto compose music that uses all music in the world
Thursday, October 22, 2009
>> 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
To wrap up:
1. Don’t trust computers
Thursday, October 22, 2009
To wrap up:
1. Don’t trust computers2. But trust us, really
Thursday, October 22, 2009
To wrap up:
1. Don’t trust computers2. But trust us, really
3. Sorry I can’t speak very well
Thursday, October 22, 2009
Thursday, October 22, 2009