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