Title: Fast, Robust Audio Fingerprinting: RARE
1Fast, Robust Audio Fingerprinting RARE
Communication, Collaboration and Signal
Processing Group
C. Burges, J. Platt, J. Goldstein, E. Renshaw
Problem Statement
How Does It Work?
Each projection maximizes the signal-to-noise
ratio on a training set. A trace (64 floats),
computed every 186 ms, is compared to a database
of fingerprints.
Given a stream of possibly noisy audio, identify
known audio clips, and their position in the
stream
Some Applications
Performance
- Allow users to identify e.g. radio music with a
PDA - Allow music players to identify whatever is
played, to provide e.g. artist, song name - Automatically identify duplicates to clean a
database - Automate music detection to compute royalties
- Detect commercials to verify that they were
aired, or to add metadata -
- System can identify any one of 240,000 clips
using 10 CPU on 850 MHz PIII - Testing with 10 ½ days of audio (3,444 songs),
using a confirmation fingerprint, gave a false
positive rate of 1.5 x 10-8 per clip, per
database entry, at a false negative rate of 0.2
per clip. Testing on 36 hours of distorted audio
gave error rates of 8 x 10-6 and 0.8
respectively.
Compute Robust Projections
Database Group