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MoodLogic Metadata MoodLogic The promise and reality of digital music The solution: Smart audio devices Key technology: Creating music metadata MoodLogic’s choice: Metadata generation with the help of end users Mining of music string data (artist name, song name, etc) Mining of individual song profiles Mining of user collections and usage logs © 2004 MoodLogic, Inc. – No reproduction or distribution without prior written permission. - Confidential - MoodLogic Metadata The promise and reality of digital music Promise: • “Music at your fingertips …” • “10,000 songs in your pocket” • “The right music at the right time …” Reality: • 10,000 songs (many of them mislabeled or miscategorized) only accessible via small screen real estate • Issue: How to get the next hour of a great music experience? - Confidential - MoodLogic Metadata The solution: Smart audio devices What is a smart audio device? • Content aware (not just portable hard disk but music device) • Ability to create music experiences “on the fly” • Adapt to the user preferences Key: music metadata + inference technology • Need for detailed descriptive data about individual songs (genre, subgenre, mood, tempo, original release year, instrumentation, etc) • Playlisting algorithms: “Play an hour of smooth Jazz with saxophone”, “Play songs similar to ‘Fight Music’ by D12” - Confidential - MoodLogic Metadata Key technology: Creating music metadata Table Of Contents Data Classification Data (Metadata) Basic TOC fields (Artist, Album, Song) Used for: Artist, Song Display Tag fixing There is no uniform database for music! Detailed classification songs Attributes (genre, mood, tempo, …) Used for: Browsing & Filtering, Playlist Creation Recommendations There is no perceptual database for music! What are the options to generate metadata? • DSP (Digital Signal Processing) • Expert ratings/ submissions • Community ratings/ submissions - Confidential - MoodLogic Metadata MoodLogic’s choice: Metadata generation with the help of end users Users listen to music and fill out detailed questionnaire describing individual songs - Confidential - MoodLogic Metadata Mining of music string data (artist name, song name, etc) • Mining 300 million submissions on (mis) spellings of artists, songs, albums • Creating of a “canonical” artist space (e.g. making sure the same artist is spelled the song the same way for all songs) • Global database requires mining of music data in different languages and different character sets Dozens of different spellings / submissions for one artist name for the same song as well as across songs by the same artist. Artist name submissions for song A: Artist name submissions for song B: Britney Spears Britny Spears Brittany Spears Brittaney Spears Britteney Spears Britney Speas Britney Spers Britney Speares … Britny Spears Brittany Spears Goal: Uniform artist entities - Confidential - Britney Spears Artist ID: 2435 MoodLogic Metadata Mining of individual song profiles • Mining > 1 billion individual song attribute ratings from end users (song model) • Assessing quality of submissions (user model) • Localization (different perceptions in different countries?) • What are the salient attributes of a song? Distribution of attribute “energy” ratings for one song (rated by 18 people) Goal: Determine song profile - Confidential - MoodLogic Metadata Mining of user collections and usage logs • Mining > 1 million user music collections (e.g. Finding like minded users) • Building and evaluating quality of recommendation systems • Determining the usefulness of product features Distribution of music collections for a million users Song IDs for user A: Song IDs for user B: 873124 243515 135123 646334 345321 246213 664343 621354 …. 243515 135123 646334 345321 246213 664343 621354 542342 …. Goal: Recommend songs - Confidential - Song ID: 24543 MoodLogic Metadata Questions? Interests? Suggestions? Thanks! - Confidential -