Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
BORM-II and UML as Accessibility Process in Knowledge and Business Modelling Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Information Retrieval with the Use of Music Clustering by Directions Algorithm

verfasst von : Adam L. Kaczmarek

Erschienen in: New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This paper introduces the Music Clustering by Directions (MCBD) algorithm. The algorithm is designed to support users of query by humming systems in formulating queries. This kind of systems makes it possible to retrieve songs and tunes on the basis of a melody recorded by the user. The Music Clustering by Directions algorithm is a kind of an interactive query expansion method. On the basis of query, the algorithm provides suggestions that may be used to enhance the query. The MCBD algorithm is bases on the Clustering by Directions (CBD) that was designed to support users of web search engines in information retrieval. The MCBD algorithm is used for retrieval of music documents similarly as the CBD algorithm is used with text documents. This paper also introduces a new kind of interface designed to perform interactive query expansion in query by humming systems. The interface is a kind of tag cloud applied to music information retrieval systems.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Cloud Storage Using Matches Algorithms
Nächstes Kapitel Using SQL Queries to Evaluate the Design of SQL Databases
Literatur
1.
A. L. Kaczmarek, “Interactive Query Expansion With the Use of Clustering by Directions Algorithm”, IEEE Transactions on Industrial Electronics, Vol. 58, No. 8 , IEEE Industrial Electronics Society, August 2011, pp. 3168-3173.
2.
A. L. Kaczmarek, “Clustering by Directions Algorithm to Narrow Search Queries”, Proceedings of the Human System Interaction Conference (HSI’08), May 25-27, 2008, IEEE, Krakow, Poland, 2008.
3.
A. Kotsifakos, P. Papapetrou, J. Hollmén, D. Gunopulos, and V. Athitsos, “A survey of query-by-humming similarity methods. In Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA ’12),” ACM, New York, NY, USA, 2012, article No. 5.
4.
Y. Zhu and D. Shasha, “Warping indexes with envelope transforms for query by humming,” Proceedings of the 2003 ACM SIGMOD international conference on Management of data (SIGMOD ’03), ACM, New York, NY, USA, 2003, pp. 181-192.
5.
S. Salvador and P. Chan, “Toward accurate dynamic time warping in linear time and space,” Intell. Data Anal, volume 11, no. 5, IOS Press Amsterdam, The Netherlands, October 2007, pp. 561-580.
6.
J. Shifrin, B. Pardo, C. Meek, and W. Birmingham, “HMM-based musical query retrieval,” Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries (JCDL ’02), ACM, New York, NY, USA, 2002, pp. 295-300.
7.
E. Unal, S. S. Narayanan, and E. Chew, “A statistical approach to retrieval under user-dependent uncertainty in query-by-humming systems,” Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval (MIR ’04), ACM, New York, NY, USA, 2004, pp. 113-118.
8.
R. B. Dannenberg and N. Hu, “Understanding Search Performance in Query-by-Humming Systems,” Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), Barcelona, Spain, October 10-14, 2004.
9.
A. Ghias, J. Logan, D. Chamberlin, and B. C. Smith, “Query by humming: Musical information retrieval in an audio database,” ACM Multimedia, ACM 1995, pp. 231-236.
10.
J.-M. Batke, G. Eisenberg, T. Sikora, P. Weishaupt, “A Query by Humming System using MPEG-7 Descriptors” Proceedings of the 116th AES Convention, Audio Engineering Society, New York, 2004.
11.
R.W. White, G. Marchionini, “Examining the effectiveness of real-time query expansion,” Inf. Process. Manage, no. 43, ACM, New York, 2007, pp. 685–704.
12.
E. N. Efthimiadis: “User choices: a new yardstick for the evaluation of ranking algorithms for interactive query expansion”, Information Processing & Management, Vol. 31, Issue 4, 1995, pp. 605-620.CrossRef
13.
E. M. Voorhees: “Query expansion using lexical-semantic relations”, Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, ACM, NY, USA, 1994, pp. 61-69.
14.
C. Seifert, B. Kump, W. Kienreich, G. Granitzer and M. Granitzer, “On the beauty and usability of tag clouds,” Proc. 12th IEEE Int. Conf. on Information Visualisation, London, UK, IEEE Jul. 2008, pp. 17-25.
15.
J.-H. Su, T.-S. Hung, C.-J. Lee, C.-L. Lu, W.-L. Chang, V. S. Tseng, “Effective Content-Based Music Retrieval with Pattern-Based Relevance Feedback,” Knowledge-Based and Intelligent Information and Engineering Systems, Lecture Notes in Computer Science, Vol. 6882, Springer 2011, pp. 285-295.
16.
Y.-H. Tseng, “Content-based retrieval for music collections,” Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR ’99). ACM, New York, NY, USA, 1999, pp. 176-182.
17.
G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Inform. Process. Manag., Vol. 24, No. 5, Elsevier, 1988, pp. 513-523.
18.
M. Szwoch, “Guido: A Musical Score Recognition System,” Proceedings of ICDAR 2007: Ninth International Conference on Document Analysis and Recognition, Vol. 2, IEEE, 2007, pp. 809-813.
Metadaten
Titel
Information Retrieval with the Use of Music Clustering by Directions Algorithm
verfasst von
Adam L. Kaczmarek
Copyright-Jahr
2015
Buch
New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering

Print ISBN: 978-3-319-06763-6

Electronic ISBN: 978-3-319-06764-3

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-06764-3_22

Neuer Inhalt

    Bildnachweise