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Music Recommendation Based on Multidimensional Description and Similarity Measures

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This study aims to create an algorithm for assessing the degree to which songs belong to genres defined a priori. Such an algorithm is not aimed at providing unambiguous classification-labelling of songs, but at producing a multidimensional description encompassing all of the defined genres. The algorithm utilized data derived from the most relevant examples belonging to a particular genre of music. For this condition to be met, data must be appropriately selected. It is based on the fuzzy logic principles, which will be addressed further. The paper describes all steps of experiments along with examples of analyses and results obtained.
Wydawca
Rocznik
Strony
325--340
Opis fizyczny
Bibliogr. 36 poz., tab., wykr.
Twórcy
autor
  • Audio Acoustics Laboratory, Faculty of Electronics, Telecomm. and Informatics, Gdańsk University of Technology, Poland
autor
  • Multimedia Systems Department, Faculty of Electronics, Telecomm. and Informatics, Gdańsk University of Technology, Poland.
Bibliografia
  • [1] Aucouturier, J.-J., Pachet, F.: Representing musical genre: A state of art, Journal of New Music Research, 32(1), 2003, 83-93.
  • [2] Bazan, J. G., Szczuka, M. S.: The Rough Set Exploration System. Transactions on Rough Sets III in: Lecture Notes in Computer Science, vol. 3400 (Peters J. F., Skowron A., Eds.), Springer Verlag, 2005, 37-56.
  • [3] Beauchamp, J. W.: Perceptually Correlated Parameters of Musical Instrument Tones, Archives of Acoustics, 36(2), 2011, 225-238.
  • [4] Benetos, E., Kotropoulos, C.: A tensor-based approach for automatic music genre classification, Proceedings European Signal Processing Conference, Lausanne, Switzerland, 2008.
  • [5] Bergstra, J., Casagrande, N., Erhan, D., Eck, D., Kegl, B.: Aggregate features and AdaBoost for music classification, Machine Learning, 65(2-3), 2006, 473-484.
  • [6] Brun, M., Johnson, Ch. D., Ramos, K. S.: Clustering: revealing intrinsic dependencies in microarray data, Genomic Signal Processing and Statistics, in: EURASIP Book Series on Signal Processing and Communication (Dougherty, E.R., Shmulevich, I., Chen, J., Wang, Z.J. Eds.), Hindawi Publishing Corporation, 2005.
  • [7] Ding, C., He, X.: Cluster Merging and Splitting in Hierarchical Clustering Algorithms, Proceedings 2nd IEEEInt’l Conf. Data Mining, Maebashi, Japan, 2002,139-146.
  • [8] Glaczynski, J., Lukasik, E.: Automatic music summarization. A “thumbnail” approach, Archives of Acoustics, 36(2), 2011, 297-309.
  • [9] Holzapfel, A., Stylianou, Y.: Musical genre classification using nonnegative matrix factorization-based features, IEEE Transactions on Audio, Speech, and Language Processing, 16(2), 2008, 424-434.
  • [10] Horner, A. B., Beauchamp, J. W., So, R. H. Y: Evaluation of Mel-Band and MFCC-Based Error Metrics for Correspondence to Discrimination of Spectrally Altered Musical Instrument Sounds, J. Audio Eng. Soc., 59(5), 2011,290-303.
  • [11] Hyoung-Gook, K., Moreau, N., Sikora, T.: MPEG-7 Audio and Beyond: Audio Content Indexing and Retrieval, Wiley & Sons, 2005.
  • [12] Kleczkowski, P.: Perception of Mixture of Musical Instruments with Spectral Overlap Removed, Archives of Acoustics, 37(3), 2012, 355-363, DOI: 10.2478/v10168-012-0045-0.
  • [13] Kostek, B.: Soft Computing in Acoustics, Applications of Neural Networks, Fuzzy Logic and Rough Sets to Musical Acoustics, Studies in Fuzziness and Soft Computing, Physica Verlag, Heildelberg, New York, 1999.
  • [14] Kostek, B., Czyzewski A.: Representing Musical Instrument Sounds for their Automatic Classification, J. Audio Eng. Soc., 49, 2001, 768-785.
  • [15] Kostek, B.: Perception-Based Data Processing in Acoustics. Applications to Music Information Retrieval and Psychophysiology of Hearing, Springer Verlag, Series on Cognitive Technologies, Berlin, Heidelberg, New York, 2005.
  • [16] Kostek, B., Kania,.: Music information analysis and retrieval techniques, Archives of Acoustics, 33(4), 2008, 483-496.
  • [17] Kostek, B., Kupryjanow, A., Zwan, P., Jiang, W., Ras, Z., Wojnarski, M., Swietlicka, J.: in: Report of the ISMIS 2011 Contest: Music Information Retrieval, Foundations of Intelligent Systems (Kryszkiewicz, M. et al. Eds.) ISMIS 2011, LNAI6804, 715-724, Springer Verlag, Berlin, Heidelberg, 2011.
  • [18] Kostek B.: Music Information Retrieval in Music Repositories, Chapter 17, in: Rough Sets and Intelligent Systems (A. Skowron, Z. Suraj, Eds.), Vol. 1, ISRL, 42, 463-489, Springer Verlag, Berlin Heilderberg, 2013.
  • [19] Kubera, E., Wieczorkowska, A. A., Ras, Z. W.: Time Variability-Based Hierarchic Recognition of Multiple Musical Instruments in Recordings, Chapter 18, in: Rough Sets and Intelligent Systems (A. Skowron, Z. Suraj, Eds.), Vol. 2, ISRL, 42, 247-363, Springer Verlag, Berlin Heilderberg, 2013.
  • [20] Kunka B., Kostek B.: Objectivization of Audio-Visual Correlation Analysis, Archives of Acoustics, 37(1), 2012, 63-72, DOI: 10.2478/v10168-012-0009-4.
  • [21] Kotsiantis, S., Kanellopoulos, D.: Discretization Techniques: A recent survey, GESTSInternational Transactions on Computer Science and Engineering, 32(1), 2006, 47-58.
  • [22] Moshkov, M. J., Skowron, A., Suraj, Z.: Extracting Relevant Information about Reduct Sets from Data Tables, Transactions on Rough Sets IX, Lecture Notes in Computer Science, Vol. 5390, 200-211,2008.
  • [23] Ness, S., Theocharis, A., Tzanetakis, G., Martins, L. G.: Improving automatic music tag annotation using stacked generalization of probabilistic SVM outputs, 17 ACM International Conf. on Multimedia, New York, NY, 2009.
  • [24] Pawlak, Z.: Rough Sets, International J. Computer and Information Sciences, 11, 1982, 341-356.
  • [25] Peters, J. F., Skowron, A. (Eds.): Transactions on Rough Sets, Lecture Notes in Computer Science, vol. 4100, Springer, 2004-2012.
  • [26] Przybyla-Kasperek, M., Wakulicz-Deja, A.: Application of Reduction of the Set of Conditional Attributes in the Process of Global Decision-making, Fundamenta Informaticae, 122(4), 2012, 327-355,2012.
  • [27] Ras, Z. W., Wieczorkowska, A. A. (Eds.): Advances in Music Information Retrieval, Series: Studies in Computational Intelligence, Vol. 274, Springer-Verlag, Berlin Heidelberg 2010, ISBN: 978-3-642-11673-5.
  • [28] Rumsey F., Semantic Audio: Machines Get Clever with Music, J. Audio Eng. Soc., 59(11), 2011, 882-887.
  • [29] Skowron, A., Stepaniuk, J., Jankowski, A., Bazan, J. G., Swiniarski, R.: Rough Set Based Reasoning About Changes, Fundamenta Informaticae, 119(3-4), 2012, 421-437.
  • [30] Sofianos, S., Ariyaeeinia, A., Polfreman, R., Sotudeh, R.: H-Semantics: A Hybrid Approach to Singing Voice Separation, J. Audio Eng. Soc., 60(10), 2012, 831-841.
  • [31] Terasawa, H., Berger, J., Makino, S.: In Search of a Perceptual Metric for Timbre: Dissimilarity Judgments among Synthetic Sounds with MFCC-Derived Spectral Envelopes, J. Audio Eng. Soc., 60(9), 2012,674-685.
  • [32] Tzanetakis G., Cook, P.: Musical genre classification of audio signal, IEEE Transactions on Speech and Audio Processing, 10(3), July 2002, 293-302.
  • [33] Wieczorkowska, A., Kubera, E., Kubik-Komar, A.: Analysis of Recognition of a Musical Instrument in Sound Mixes Using Support Vector Machines, Fundamenta Informaticae, 107(1), 2011, 85-104.
  • [34] Wolkowicz, J., Kulka, Z., Keselj, V.: n-gram-based approach to composer recognition, Archives of Acoustics, 33(1), 2008, 43-55.
  • [35] Zheng, L., Li, T., Ding, C.: Hierarchical Ensemble Clustering, Proceedings of 2010 IEEE International Conference on Data Mining (ICDM 2010), 2010.
  • [36] Zwan, P., Kostek, B.: System for Automatic Singing Voice Recognition, J. Audio Eng. Soc., 56(9), 2008, 710-723.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ca448ee4-bfb2-41f5-8e3f-157bfa4c15c3
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