Song Intersection by Approximate Nearest Neighbours Overview The Need as long as Normalization Specificity Spectrum Remixes of One Title

Song Intersection by Approximate Nearest Neighbours Overview The Need as long as Normalization Specificity Spectrum Remixes of One Title www.phwiki.com

Song Intersection by Approximate Nearest Neighbours Overview The Need as long as Normalization Specificity Spectrum Remixes of One Title

Williams, Randy, Host;Producer has reference to this Academic Journal, PHwiki organized this Journal 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 – R in addition to omized projections Results Best features 2018 song subset The Need as long as 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 songs Remixes Look as long as specific exact matches Bag of Features model Our work (nearest neighbor) Fingerprinting Genre Remixes of One Title Remix Examples Abba Gimme Gimme Madonna Hung Up Tracy Young Remix of Hung Up Tracy Young Remix 2 of 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 in addition to 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 R in addition to om Projections R in addition to om projections estimate distance Multiple projections improve estimate

Locality Sensitive Hashing Hash function is a r in addition to om projection No pair-wise computation Collisions are nearest neighbors Distant Vector Distant Vector Remix Nearest Neighbour Algorithm 1 Extract database audio shingles Eliminate shingles < song’s mean power Compute remix distance as long as all pairs Choose pairs with remix distance < r0 Remix Nearest Neighbour Algorithm Revisited Extract database audio shingles Eliminate shingles < song’s mean power Hash remaining shingles, bin width=r0 Collisions are near neighbour shingles 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 as long as ce distances too expensive LSH is 1-2 orders of magnitude faster LSH Remix Recognition is Accurate Conclusions Remixes are hard, but well-posed Brute as long as ce distances too expensive LSH is 1-2 orders of magnitude faster LSH Remix Recognition is Accurate Conclusions Remixes are hard, but well-posed Brute as long as ce distances too expensive LSH is 1-2 orders of magnitude faster LSH Remix Recognition is Accurate Conclusions Remixes are hard, but well-posed Brute as long as ce distances too expensive LSH is 1-2 orders of magnitude faster LSH Remix Recognition is Accurate Williams, Randy Great Awakening- WHBB-AM, The Host;Producer www.phwiki.com

Williams, Randy Host;Producer

Williams, Randy is from United States and they belong to Great Awakening- WHBB-AM, The and they are from  Selma, United States got related to this Particular Journal. and Williams, Randy deal with the subjects like Country Music; Local News

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This Particular Journal got reviewed and rated by Addis Ababa University and short form of this particular Institution is ET and gave this Journal an Excellent Rating.