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Searching and Classifying Affinities in a Web Music Collection

  • Nicola OrioEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 701)

Abstract

Online music libraries available on the Web contain a large amount of audio content that is usually the result of digitization of analogue recordings or the direct acquisition of digital sources. The acquisition process is carried out by several persons and may last a number of years, thus it is likely that the same or similar audio content is present in different versions. This paper describes a number of possible similarities, which are called affinities, and presents a methodology to detect the kind of affinity from the automatic analysis and matching of the audio content.

Keywords

Lossy Compression Locality Sensitive Hashing Pure Listening Audio Track Audio Content 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The author wishfully thanks the company LaCosa s.r.l. for granting access to a large digital audio collection, which has been the basis for the tests, and for providing useful insights on how interpreting the results.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Cultural HeritageUniversity of PaduaPaduaItaly

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