song intersection by approximate nearest neighbours michael casey, goldsmiths malcolm slaney, yahoo!...
TRANSCRIPT
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Song Intersection by Approximate Nearest Neighbours
Michael Casey, Goldsmiths
Malcolm Slaney, Yahoo! Inc.
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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
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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
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Specificity Spectrum
Cover songsRemixes
Look for specificexact
matches
Bag of Features
model
Our work(nearestneighbor)
Fingerprinting Genre
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Remixes of One Title
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Remix Examples
Abba Gimme Gimme
Madonna Hung Up
Tracy Young Remixof Hung Up
Tracy Young Remix 2of Hung Up
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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
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Audio Processing
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Remix Distance
N-best matches Matched filter(implemented as nearest neighbor)
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Choosing r0
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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
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Random Projections
• Random projections estimate distance
• Multiple projections improve estimate
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Locality Sensitive Hashing
• Hash function is a random projection
• No pair-wise computation
• Collisions are nearest neighbors Distant Vector
Distant Vector
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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
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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
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Method
• Choose 20 Query Songs
• Each has 3-10 Remixes
• 306 Madonna Songs
• 2018 Madonna+Miles
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Results
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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
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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
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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
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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