RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

Two algorithms, RecurSIA and RRT,are presented,designed to increase the compression factor achieved using SIATEC-based point-set cover algorithms. RecurSIA recursively applies a TEC cover algorithm to the patterns intheTECs that it discovers. RRT attempts to remove translators from each TEC without reducing its covered set. When evaluated with COSIATEC, SIATECCompress and Forth’s algorithm on the JKU Patterns Development Database, using RecurSIA with or without RRT increased compression factor and recall but reduced precision. Using RRT alone increased compression factor and reduced recall and precision, but had a smaller effect than RecurSIA.
Original languageEnglish
Title of host publication12th International Workshop on Machine Learning and Music (MML 2019)
Number of pages6
PublisherSpringer
Publication statusAccepted/In press - 2019
EventInternational Workshop on Machine Learning and Music - Würzburg, Germany
Duration: 16 Sep 201916 Sep 2019
Conference number: 12
https://musml2019.weebly.com/

Conference

ConferenceInternational Workshop on Machine Learning and Music
Number12
CountryGermany
CityWürzburg
Period16/09/201916/09/2019
Internet address

Keywords

  • machine learning
  • music analysis
  • data compression
  • point-set patterns
  • pattern discovery
  • minimum description length
  • algorithms

Cite this

Meredith, D. (Accepted/In press). RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators. In 12th International Workshop on Machine Learning and Music (MML 2019) Springer.
Meredith, David. / RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators. 12th International Workshop on Machine Learning and Music (MML 2019). Springer, 2019.
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title = "RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators",
abstract = "Two algorithms, RecurSIA and RRT,are presented,designed to increase the compression factor achieved using SIATEC-based point-set cover algorithms. RecurSIA recursively applies a TEC cover algorithm to the patterns intheTECs that it discovers. RRT attempts to remove translators from each TEC without reducing its covered set. When evaluated with COSIATEC, SIATECCompress and Forth’s algorithm on the JKU Patterns Development Database, using RecurSIA with or without RRT increased compression factor and recall but reduced precision. Using RRT alone increased compression factor and reduced recall and precision, but had a smaller effect than RecurSIA.",
keywords = "machine learning, music analysis, data compression, point-set patterns, pattern discovery, minimum description length, algorithms",
author = "David Meredith",
year = "2019",
language = "English",
booktitle = "12th International Workshop on Machine Learning and Music (MML 2019)",
publisher = "Springer",
address = "Germany",

}

Meredith, D 2019, RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators. in 12th International Workshop on Machine Learning and Music (MML 2019). Springer, International Workshop on Machine Learning and Music, Würzburg, Germany, 16/09/2019.

RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators. / Meredith, David.

12th International Workshop on Machine Learning and Music (MML 2019). Springer, 2019.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

TY - GEN

T1 - RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators

AU - Meredith, David

PY - 2019

Y1 - 2019

N2 - Two algorithms, RecurSIA and RRT,are presented,designed to increase the compression factor achieved using SIATEC-based point-set cover algorithms. RecurSIA recursively applies a TEC cover algorithm to the patterns intheTECs that it discovers. RRT attempts to remove translators from each TEC without reducing its covered set. When evaluated with COSIATEC, SIATECCompress and Forth’s algorithm on the JKU Patterns Development Database, using RecurSIA with or without RRT increased compression factor and recall but reduced precision. Using RRT alone increased compression factor and reduced recall and precision, but had a smaller effect than RecurSIA.

AB - Two algorithms, RecurSIA and RRT,are presented,designed to increase the compression factor achieved using SIATEC-based point-set cover algorithms. RecurSIA recursively applies a TEC cover algorithm to the patterns intheTECs that it discovers. RRT attempts to remove translators from each TEC without reducing its covered set. When evaluated with COSIATEC, SIATECCompress and Forth’s algorithm on the JKU Patterns Development Database, using RecurSIA with or without RRT increased compression factor and recall but reduced precision. Using RRT alone increased compression factor and reduced recall and precision, but had a smaller effect than RecurSIA.

KW - machine learning

KW - music analysis

KW - data compression

KW - point-set patterns

KW - pattern discovery

KW - minimum description length

KW - algorithms

UR - https://arxiv.org/abs/1906.12286

UR - https://github.com/chromamorph/omnisia-recursia-rrt-mml-2019

M3 - Article in proceeding

BT - 12th International Workshop on Machine Learning and Music (MML 2019)

PB - Springer

ER -

Meredith D. RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators. In 12th International Workshop on Machine Learning and Music (MML 2019). Springer. 2019