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MHD London
 

MHD London

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Gracenote Dev slides presented at MHD London 2013

Gracenote Dev slides presented at MHD London 2013

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  • Get to wear matching shirts! Drink Red Stripe! Bass solo! Best side of the Bay.

MHD London MHD London Presentation Transcript

  • MusicHackDay, London Derek Tingle @dtingle 1
  • You might know us from this... 2
  • We’ve Moved Beyond the Shiny Disc Music Recognition in phones, Apps and Cloud services – from iTunes and Amazon to Rhapsody and musiXmatch Discovery and Playlisting technology and Cover Art in millions of cars TV Listings and Video Recognition in Smart TVs and Apps 3
  • Music APIs Video APIs Music Recognition and Discovery technology and the largest source of music metadata and Cover Art Automatic Content Recognition (ACR) for Second Screen Apps. Plus, TV listings data and Imagery 4
  • Music APIs Video APIs Music Recognition and Discovery technology and the largest source of music metadata and Cover Art Automatic Content Recognition (ACR) for Second Screen Apps. Plus, TV listings data and Imagery 5
  • File Recognition ○ Acoustic fingerprint recognizes audio files ○ Used to retrieve metadata and related content from Gracenote database ○ Can also be used to “unlock” content in cloud lockers 6
  • Streaming Recognition ○ “What’s that song???” ○ Robust audio fingerprint can tolerate environmental noise ○ Ideal for mobile devices ○ Provides associated metadata and enriched content 7
  • Rich Music Metadata and Descriptors Phoenix - “Entertainment” 8
  • Rich Music Metadata and Descriptors Phoenix - “Entertainment” Mid Tempo, Energetic Dreamy song by a Parisian Indie Pop band from the 2000’s 9
  • Rich Music Metadata and Descriptors Phoenix - “Entertainment” Mid Tempo, Energetic Dreamy song by a Parisian Indie Pop band from the 2000’s 10
  • Rich Music Metadata and Descriptors Phoenix - “Entertainment” Mid Tempo, Energetic Dreamy song by a Parisian Indie Pop band from the 2000’s 11
  • Rich Music Metadata and Descriptors Phoenix - “Entertainment” Mid Tempo, Energetic Dreamy song by a Parisian Indie Pop band from the 2000’s The French group Phoenix draw elements from their eclectic '80s upbringing to arrive at a satisfying blend of rock and synthesizers. Vocalist Thomas Mars, bassist Deck d'Arcy, and guitarist… 12
  • Over 2000 Genres Metadata 13
  • 100 Mood Descriptors 14
  • Music APIs • • • Search by Artist, Album or Track Rich descriptors, cover art, biographies, and more… Wrappers in Python, Javascript, Ruby, Java, PHP • • • • iOS and Android SDKs Full search and metadata from Web API Audio file recognition Audio streaming recognition • • • • C library for Win/Mac/Linux Full search and metadata from Web API File and streaming recognition Playlist and discovery 15
  • What can I do with Gracenote APIs? 16
  • Habu – Coachella 2013 ○ Used Gracenote mood data to create a “mood map” of each day’s lineup at Coachella 2013 ○ habu app creates one-click mood-based playlists 17
  • Hella Bar ○ Uses Gracenote Taste Profile API and Yelp data to recommend cafes, bars, and restaurants that fit your musical taste ○ By Oscar Celma and Alex Passant of Seevl.fm at Hella Hack Oakland 18
  • Fuwari ○ Mood-grid based mobile app with beautiful iOS7-inspired design ○ See what moods your Facebook friends are listening to ○ Winner of TechCrunch Tokyo Hackathon, Finalist at Mashup Awards 9 19
  • Experimental API Timeline Metadata Adaptive Radio 20
  • Timeline Metadata ○ Segmentation • Divides songs into verse/chorus/etc segments ○ Beat and Onset Detection ○ Dynamic Moods • Track moods as they change through the song 21
  • Timeline Metadata ○ https://github.com/gracenotedev/timeline-metadata-api 22
  • Adaptive Radio ○ ○ ○ ○ ○ ○ RESTful radio API JSON or XML Artist or track seeds Adapts to play and skip events Rich metadata Deezer IDs 23
  • Gracenote Prize 2 x UDOO 2 x Timbuk2 Backpack 24
  • We’re Hiring 25
  • Questions? https://developer.gracenote.com @GracenoteDev , @dtingle 26