mhd london
DESCRIPTION
Gracenote Dev slides presented at MHD London 2013TRANSCRIPT
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MusicHackDay, London
Derek Tingle @dtingle
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You might know us from this...
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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
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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
Music APIs Video APIs
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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
Music APIs Video APIs
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○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
File Recognition
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○“What’s that song???”
○Robust audio fingerprint can tolerate environmental noise
○Ideal for mobile devices
○Provides associated metadata and enriched content
Streaming Recognition
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Rich Music Metadata and DescriptorsPhoenix - “Entertainment”
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Rich Music Metadata and DescriptorsPhoenix - “Entertainment”
Mid Tempo, Energetic Dreamy song by a Parisian Indie Pop band
from the 2000’s
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Rich Music Metadata and DescriptorsPhoenix - “Entertainment”
Mid Tempo, Energetic Dreamy song by a Parisian Indie Pop band
from the 2000’s
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Rich Music Metadata and DescriptorsPhoenix - “Entertainment”
Mid Tempo, Energetic Dreamy song by a Parisian Indie Pop band
from the 2000’s
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Rich Music Metadata and DescriptorsPhoenix - “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…
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Metadata2000 GenresOver
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100 Mood Descriptors
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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
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What can I do with Gracenote APIs?
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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
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○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
Hella Bar
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○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
Fuwari
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Experimental API
Timeline MetadataAdaptive Radio
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Timeline Metadata○Segmentation• Divides songs into verse/chorus/etc segments
○Beat and Onset Detection○Dynamic Moods• Track moods as they change through the song
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Timeline Metadata
○https://github.com/gracenotedev/timeline-metadata-api
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Adaptive Radio
○RESTful radio API○JSON or XML○Artist or track seeds○Adapts to play and skip events○Rich metadata○Deezer IDs
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Gracenote Prize
2 x UDOO 2 x Timbuk2 Backpack
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We’re Hiring
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https://developer.gracenote.com@GracenoteDev , @dtingle
Questions?