The future music platform

The future music platform Brian Whitman @bwhitman co-Founder & CTO, The Echo Nest

If by “fail” you mean “become the future of the industry”, OK.

Hello! I “teach computers to listen to music”

“Fish / Cut bait I’m a often-frustrated musician & technology person

Making something from everything

Music retrieval and “music intelligence” applications creepy Hit prediction Taste / recommendation Automated composition Classiﬁcation, tagging Feature extraction (key, tempo) Search & retrieval

4 · Elias Pampalk air air air air air avemaria avemaria avemaria avemaria avemaria eifel65−blue eifel65−blue eifel65−blue branden branden branden branden gowest eifel65−blue gowest eifel65−blue gowest elise elise elise elise elise branden branden vm−bach vm−bach vm−bach frozen frozen frozen frozen lovsisintheair gowest fromnewyorktola lovsisintheair gowest fromnewyorktola lovsisintheair gowest fromnewyorktola lovsisintheair radio fromnewyorktola radio radio fromnewyorktola kidscene kidscene kidscene vm−bach vm−bach radio radio supertrouper supertrouper supertrouper kidscene kidscene supertrouper supertrouper Retrieving Music by Rhythmic Similarity mond mond mond mond mond beethoven beethoven beethoven beethoven fuguedminor fuguedminor fuguedminor fuguedminor therose therose therose therose therose future future future future dancingqueen dancingqueen dancingqueen dancingqueen manicmonday manicmonday manicmonday manicmonday lovemetender lovemetender lovemetender lovemetender lovemetender lovemetender vm−brahms vm−brahms vm−brahms vm−brahms memory memory memory memory memory memory ga−doedel ga−doedel ga−doedel ga−doedel ga−doedel ga−doedel fatherandson fatherandson fatherandson fatherandson conga conga conga conga conga conga 1.5 rainbow rainbow rainbow rainbow ga−japan ga−japan ga−japan ga−japan foreveryoung foreveryoung foreveryoung foreveryoung foreveryoung foreveryoung mindfiels mindfiels mindfiels mindfiels threetimesalady threetimesalady threetimesalady threetimesalady nma−bigblue nma−bigblue nma−bigblue nma−bigblue nma−bigblue nma−bigblue Computers “helping” you discover music eternalflame eternalflame eternalflame eternalflame eternalflame eternalflame feeling feeling feeling feeling feeling revolution revolution revolution revolution revolution ironic ironic ironic ironic ironic californiadream californiadream californiadream californiadreamunbreak− californiadreamunbreak− californiadreamunbreak− firsttime unbreak− firsttime firsttime firsttime firsttime unbreak− unbreak− myheart myheart myheart risingsun myheart risingsun risingsun risingsun risingsun risingsun myheart myheart sexbomb sexbomb sexbomb sexbomb sexbomb 110 bpm americanpie americanpie americanpie rhcp− rhcp− rhcp− drummerboy angels drummerboy angels drummerboy angels drummerboy angels drummerboy angels angels ga−nospeech rhcp− ga−nospeech rhcp− ga−nospeech rhcp− ga−nospeech rhcp− bfmc−freestyler rhcp− bfmc−freestyler rhcp− bfmc−freestyler 112 bpm californication rhcp− bfmc−freestyler californication rhcp− bfmc−freestyler californication world sl−summertime friend friend friend friend friend friend missathing missathing missathing missathing missathing torn californication world sl−summertime torn torn torn torn torn californication world sl−summertime world sl−summertime world sl−summertime 1 sl−whatigot sl−whatigot sl−whatigot sl−whatigot sl−whatigot squared Euclidean distance yesterday−b yesterday−b yesterday−b yesterday−b 114 bpm 120 bpm addict addict addict addict addict ga−lie ga−lie ga−lie ga−lie ga−lie ga−lie limp−nobody limp−nobody limp−nobody limp−nobody limp− limp− limp− limp− limp− bongobong bongobong bongobong bongobong bongobong bongobong 122 bpm 116 bpmnewyork newyork newyork newyork newyork newyork pr−broken pr−broken pr−broken pr−broken pr−broken pr−broken n2gether themangotree n2gether themangotree n2gether themangotree n2gether themangotree n2gether themangotree n2gether themangotree Musical Genres (Classical, Country…..) the combined sml−adia sml−adia sml−adia sml−adia sml−adia sml−adia Fig. IV GenreGram set described in this paper was used. For the Classical 124 bpm Fig. V GenreSpace korn−freak korn−freak korn−freak korn−freak korn−freak korn−freak bfmc−instereo bfmc−instereo bfmc−instereo bfmc−instereo bfmc−instereo bfmc−instereo cocojambo cocojambo cocojambo cocojambo cocojambo cocojambo Orchestra, Piano…) only the Music Surface features were bigworld ga−iwantit limp−pollution bigworld bigworld limp−pollution bigworld ga−iwantit limp−pollution bigworld bigworld ga−iwantit limp−pollution limp−pollution limp−pollution lovedwoman ga−iwantit pr−deadcell lovedwoman ga−iwantit pr−deadcell lovedwoman ga−iwantit pr−deadcell pr−deadcell macarena macarena macarena macarena macarena macarena bfmc−rocking bfmc−rocking bfmc−rocking bfmc−rocking bfmc−rocking bfmc−skylimit bfmc−skylimit bfmc−skylimit lovedwoman lovedwoman lovedwoman pr−deadcell pr−deadcell rockdj rockdj rockdj bfmc−skylimit bfmc−skylimit d for the Speech Genres (MaleVoice, FemaleVoice…) pr−revenge pr−revenge pr−revenge pr−revenge pr−revenge rockdj rockdj rockdj bfmc−uprocking bfmc−uprocking bfmc−uprocking bfmc−uprocking 0.5 126 bpm uency cepstral coefficients 5. USER INTERFACES [16] (MFCC) were used. are features commonly used in speech recognition 6. FUTURE WORK Two new graphical user interfaces for browsing and interacting An obvious direction for future research is to expand the . with collections of audio signals have been 2. bpm of (Figure developed Fig. 128 Islands Music representing a 7x7 SOM with in pieces of music. The artists and full titles of hierarchy both 77 width and depth. The combinati M u sic IV,V) . They are based on the extracted feature vectors and by the shortsegmentation can be with on the web page or inclassification which are represented the identifiers here, [20] found automatic genre the thesis. automatic genre classification results. provide a way to browse audio to locate regions of in ssic a l C o u n try D isc o H ip H o p Ja z z R o c k 5. CONCLUSION 130 bpm Another interesting direction is the combination of the gra • GenreGram is a dynamic real-time 0 audio display for e stra P ia n o C h o ir S trin g Q u a rte t The Islands of genre have not yetinterfaces118level 116 with automatic 110 showing automatic genre classification results. Each Music is 130 128 126 124 user reached adescribed 114 122 120 which would112 similarity retriev suggest their commerci represented as a cylinder that moves up and they demonstrate the possibility of such systems and serve well as a tool fo however, down in real takes into(bpm) Tempo account the automatic genre classification. In its research. Any part of the system can be modified or replaced can not be performed in re form the beat analysis algorithm and the resulting effects c

Columbia University, NYC Academic lifestyle MIT Media Lab ﬁnishing my dissertation

Always do it automatically, on everything

Started a company - are you sure you want to do this?

The Echo Nest 2010 Somerville, MA USA 30 people 200 computers Lots of products 5,000,000,000 documents 1,200,000 artists 20,000,000 songs Where we’ve gotten in the past few years

Good news! We’re on the right track

We are a music application development platform that knows more about music content and music consumers than anyone.

The platform

You’re a music fan. How do you get to music? Or inversely, you’re a musician. How do you get to your fans?

Only 10 years ago - pretty terrible. Buy a thing or borrow it.

5 years ago - a little better. Easier to self publish and discover.

Today. The choices for distribution are immense.

App developers are now the future of the music business.

Good news: thousands of developers working on music. Bad news: most have been locked out of the business.

Until now!

Music platform Content Engineering Audience What a music platform gives you

Music platform Who can do what Content Engineering Audience

The power of APIs

This is the Echo Nest’s business!

Fingerprint Acoustic Artist metrics analysis Metadata Content / Similarity Streaming Feeds Predictive analytics Recommendation Search / Tags So many possibilities all built into the plaftorm

Not just independents-- the power is from partnerships discovery playlisting mobile music games music visualizers remix apps analytics targeted marketing branded entertainment 100s of apps to date vibrant, active developer API developer community

Where can I get audio from? Content / Streaming

Probably shouldn’t play YouTube videos Content / Streaming

Or stream from blogs Content / Streaming

Content / Streaming But the distributors & labels are getting there!

They believe in this as much as we do. Content / Streaming

This Pandora competitor was built in 24 hours by one developer. It has 10 million songs available. Artist Mood Key Tempo Popularity Familiarity Location

An employee built this algorithm during a MHD Within a week there were 3 competing web apps for it.

audiokicker is an upcoming iPhone app for social music

audiokicker uses Echo Nest for recommendation and 7digital for content

EN client streaming service Thumbplay

All the potential is a bit bafﬂing. This very inspired person is building “Frasier”-themed experiences. http://music.joshmillard.com/2010/06/04/nine-inch-niles-the-seattleward-spiral/

There are hundreds of apps available on the platform already From big companies to one-person ideas

Someone in this room is going to invent the way music is experienced in the future.

http://notes.variogr.am	2936
http://blog.echonest.com	230
http://translate.googleusercontent.com	3
http://www.slaytercreative.com	3
http://static.slidesharecdn.com	3
http://us-w1.rockmelt.com	2
http://webcache.googleusercontent.com	1
http://127.0.0.1:8795	1
http://jfosho.tumblr.com	1
http://www.twylah.com	1
http://redirectit.itelcel.com.clon-games.appspot.com	1

The future music platform

by Brian Whitman on Oct 22, 2010

Accessibility

Categories

Tags

Upload Details

Usage Rights

11 Embeds 3,182

Statistics

The future music platform — Presentation Transcript