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Tytuł artykułu

Automatic Music Summarization.A "Thumbnail" Approach

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the paper, various approaches to automatic music audio summarization are discussed. The project described in detail, is the realization of a method for extracting a music thumbnail – a fragment of continuous music of a given duration time that is most similar to the entire music piece. The results of subjective assessment of the thumbnail choice are presented, where four parameters have been taken into account: clarity (representation of the essence of the piece of music), conciseness (the motifs are not repeated in the summary), coherence of music structure, and overall quality of summary usefulness.
Rocznik
Strony
297--309
Opis fizyczny
Bibliogr. 20 poz., tab., wykr.
Twórcy
autor
  • Poznań University of Technology Faculty of Computing Science Institute of Computing Science Piotrowo 3A, 60-965 Poznań, Poland, Ewa.Lukasik@cs.put.poznan.pl
Bibliografia
  • 1. Anioła P., Łukasik E. (2007), JAVA library for automatic musical instruments recognition, AES Convention Paper 7157.
  • 2. Bartsch M.A., Wakefield G.H. (2001), To Catch a Chorus: Using Chroma-based Representations for Audio Thumbnailing, Proc. WASPA.
  • 3. Chai W. (2006), Semantic segmentation and summarization of music: methods based on tonality and recurrent structure, Signal Processing Magazine, IEEE, 23, 2, 124-132.
  • 4. Clifford R., Christodoulakis M., Crawford T., Meredith D., Wiggins G. (2006), A fast, randomised, maximum subset matching algorithm for document-level music retrieval, Proc. ISMIR.
  • 5. Cooper M., Foote J. (2002), Automatic Music Summarization via Similarity Analysis, Proc. ISMIR.
  • 6. Dannenberg R.B., Hu N. (2002), Pattern discovery techniques for music audio, Proc. ISMIR.
  • 7. Dropik Ł., Łukasik E. (2010), Two-Level Hierarchical Classification of Music Genre for Music Social Networks, Foundations of Computer and Decision Sciences, 35, 4.
  • 8. Foote J. (1999), Visualizing Music and Audio using Self-Similarity, Proc. ACM Multimedia 99, pp. 77-80.
  • 9. Goto M.A. (2003), Chorus-Section Detecting Method for Musical Audio Signals, Proc. IEEE ICASSP.
  • 10. Kelly C., Gainza M., Dorran D., Coyle E. (2010), Audio Thumbnail Generation of Irish Traditional Music, Irish Systems and Signals Conference, Cork.
  • 11. Kostek B., Kania Ł. (2008), Music information analysis and retrieval techniques, Archives of Acoustics, 33, 4, 483-496.
  • 12. Logan B., Chou S. (2000), Music Summarization Using Key Phrases, Proc. IEEE ICASSP.
  • 13. Łukasik E. (2005), Wavelet Packets Features Extraction and Selection for Discriminating Plucked Sounds of Violins, Lecture Notes "Advances in Soft Computing", Springer-Verlag, pp. 867-875.
  • 14. Łukasik E. (2010), Long Term Cepstral Coefficients for violin identification, 128 AES Convention Paper 8132, London.
  • 15. Meredith D., Lernstrom K., Wiggins G.A. (2002), Algorithms for discovering repeated patterns inmultidimensional representations of polyphonic music, Journal of New Music Research, 31, 4, 321-345.
  • 16. Peeters G., Burthe A., Rodet X. (2002), Toward automatic music audio summary generation from signal analysis, Proc. ISMIR.
  • 17. Rakowski A. (2009), The domain of pitch in music, Archives of Acoustics, 34, 4, 429-443.
  • 18. Xu Ch., Maddage N.C., Shao X. (2005), Automatic Music Classification and Summarization, IEEE Transactions on Speech and Audio Processing, 13, 3, 441-450.
  • 19. Xu J.P., Zhao Y., Chen Z. (2009), Music snippet extraction via melody-based repeated pattern discovery, Sci China Ser F-Inf Sci, 52, 5, 804-812.
  • 20. Yang C. (2001), MACS: Music Audio Characteristic Sequence Indexing for Similarity Retrieval, Proc. of WASPAA.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS8-0020-0020
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