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Published byIsaiah Morales Modified over 2 years ago

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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 songs rating to another –– > Better matches Playlists –Find matches to user requests –Remove adult/child music Search results –Dont show duplicates

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Specificity Spectrum Cover songsRemixes Look for specific exact matches Bag of Features model Our work (nearest neighbor) FingerprintingGenre

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Remixes of One Title

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Remix Examples Abba Gimme Gimme Madonna Hung Up Tracy Young Remix of Hung Up Tracy Young Remix 2 of 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

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Remix Nearest Neighbour Algorithm 1 1.Extract database audio shingles 2.Eliminate shingles < songs 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 < songs 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

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