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Song Intersection by Approximate Nearest Neighbours
Michael Casey, Goldsmiths
Malcolm Slaney, Yahoo! Inc.
Overview
• Large Databases: Everywhere!– 8B web pages– 50M audio files on web– 2M songs
• Find duplicates with shingles– Text-based – LSH - Randomized projections
• Results – Best features– 2018 song subset
The Need for Normalization
• Recommendations– Apply one song’s rating to another– – > Better matches
• Playlists– Find matches to user requests– Remove adult/child music
• Search results– Don’t show duplicates
Specificity Spectrum
Cover songsRemixes
Look for specificexact
matches
Bag of Features
model
Our work(nearestneighbor)
Fingerprinting Genre
Remixes of One Title
Remix Examples
Abba Gimme Gimme
Madonna Hung Up
Tracy Young Remixof Hung Up
Tracy Young Remix 2of Hung Up
How Remix Recognition Works
• Algorithm– Matched filter best (ICASSP2005 result)
– Nearest neighbor in 360–1200D space• Ill posed?
• Efficient implementation– Audio shingles– Like web-duplicate search– Locality-sensitive hashing– Probabilistic guarantee
Audio Processing
Remix Distance
N-best matches Matched filter(implemented as nearest neighbor)
Choosing r0
Hashing
• Types of hashes– String : put casey vs cased in different bins– Locality sensitive : find nearest neighbors
• High-dimensional and probabilistic
• Two Nearest Neighbor implementations– Pair-wise distance computation
– 1,000,000,000,000 comparisons in 2M song database
– Hash bucket collisions– 1,000,000,000 hash projections
Random Projections
• Random projections estimate distance
• Multiple projections improve estimate
Locality Sensitive Hashing
• Hash function is a random projection
• No pair-wise computation
• Collisions are nearest neighbors Distant Vector
Distant Vector
Remix Nearest Neighbour Algorithm 1
1.Extract database audio shingles
2.Eliminate shingles < song’s mean power
3.Compute remix distance for all pairs
4.Choose pairs with remix distance < r0
1.Extract database audio shingles
2.Eliminate shingles < song’s mean power
3.Hash remaining shingles, bin width=r0
4.Collisions are near neighbour shingles
Remix Nearest Neighbour Algorithm Revisited
Method
• Choose 20 Query Songs
• Each has 3-10 Remixes
• 306 Madonna Songs
• 2018 Madonna+Miles
Results
Conclusions
• Remixes are hard, but well-posed
• Brute force distances too expensive
• LSH is 1-2 orders of magnitude faster
• LSH Remix Recognition is Accurate
Conclusions
• Remixes are hard, but well-posed
• Brute force distances too expensive
• LSH is 1-2 orders of magnitude faster
• LSH Remix Recognition is Accurate
Conclusions
• Remixes are hard, but well-posed
• Brute force distances too expensive
• LSH is 1-2 orders of magnitude faster
• LSH Remix Recognition is Accurate
Conclusions
• Remixes are hard, but well-posed
• Brute force distances too expensive
• LSH is 1-2 orders of magnitude faster
• LSH Remix Recognition is Accurate