1. - Confidential - MoodLogic Metadata MoodLogic © 2004 MoodLogic, Inc. – No reproduction or distribution without prior written permission. 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

2. - 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?

3. - 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 ”

4. - Confidential - MoodLogic Metadata Key technology: Creating music metadata Table Of Contents Data Basic TOC fields (Artist, Album, Song) Used for: Artist, Song Display Tag fixing Classification Data (Metadata) Detailed classification songs Attributes (genre, mood, tempo, … ) Used for: Browsing & Filtering, Playlist Creation Recommendations What are the options to generate metadata? DSP (Digital Signal Processing) Expert ratings/ submissions Community ratings/ submissions There is no uniform database for music!There is no perceptual database for music!

5. - 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

6. - 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 Artist name submissions for song A: Britney Spears Britny Spears Brittany Spears Brittaney Spears Britteney Spears Britney Speas Britney Spers Britney Speares … Dozens of different spellings / submissions for one artist name for the same song as well as across songs by the same artist. Goal: Uniform artist entities Britney Spears Artist ID: 2435 Artist name submissions for song B: Britny Spears Brittany Spears

7. - Confidential - 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

8. - Confidential - MoodLogic Metadata Mining of user collections and usage logs Distribution of music collections for a million users 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 Song IDs for user A: 873124 243515 135123 646334 345321 246213 664343 621354 …. Song IDs for user B: 243515 135123 646334 345321 246213 664343 621354 542342 …. Goal: Recommend songs Song ID: 24543

9. - Confidential - MoodLogic Metadata Questions? Interests? Suggestions? Thanks!