Music information analysis and retrieval techniques

• Autorzy: Kostek B. , Kania Ł.
• Języki publikacji: EN

Abstrakty

EN

This paper presents the main issues related to music information retrieval (MIR) domain. MIR is a multi-discipline area. Within this domain, there exists a variety of approaches to musical instrument recognition, musical phrase classification, melody classification (e.g. query-by-humming systems), rhythm retrieval, high-level-based music retrieval such as looking for emotions in music or differences in expressiveness, music search based on listeners' preferences, etc. The key-issue lies, however, in the parameterization of a musical event. In this paper some aspects related to MIR are shortly reviewed in the context of possible and current applications to this domain.

Słowa kluczowe

EN

music; music information retrieval; music exploration systems; multimedia databases

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2008
• Tom: Vol. 33, No. 4
• Strony: 483--496
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Kostek B.

autor

Kania Ł.

• Gdańsk University of Technology, Multimedia Systems Department, Narutowicza 11/12, 80-952 Gdańsk, Poland,

Bibliografia

• [1] BURGES C.J.C., A Tutorial on Support Vector Machines for Pattern Recognition, Kluwer Academic Publisher, Boston 1998.
• [2] CHUDY M., Automatic identification of music performer using the linear prediction cepstral coefficients method, Archives of Acoustics, 33, 1, 27–33 (2008).
• [3] DZIUBINSKI M., KOSTEK B., Octave Error Immune and Instantaneous Pitch Detection Algorithm, J. New Music Research, 34, 273–292 (2005).
• [4] http://www.chiariglione.org/MPEG/standards/mpeg-7/mpeg-7.htm#E12E46 (information onMPEG 7 standard).
• [5] http://ismir2007.ismir.net (International Conference on Music Information Retrieval website).
• [6] http://weka.classifiers.functions.supportVector (information on SVM implemented in WEKA system).
• [7] http://www.cs.waikato.ac.nz/ml/weka/ (information on WEKA classifier package).
• [8] http://www.soft-computing.de/def.html (information on soft computing methods).
• [9] http://svm.sdsc.edu/svm-overview.html (information on SVM).
• [10] KIM H.-G., BURRED J., SIKORA T., How efficient is MPEG-7 for general sound recognition, Proceedings of the Audio Engineering Society (AES04) International Conference, London. Retrieved, March 16, 2004.
• [11] 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.
• [12] 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.
• [13] KOSTEK B., CZY˙Z EWSKI A., Representing Musical Instrument Sounds for their Automatic Classification, J. Audio Eng. Soc., 49, 768–785 (2001).
• [14] KOSTEK B., WIECZORKOWSKA A., Parametric representation of musical sounds, Archives of Acoustics, 22, 1, 3–26 (1997).
• [15] KOSTEK B., KRÓLIKOWSKI R., Application of artificial neural networks to the recognition of musical sounds, Archives of Acoustics, 22, 1, 27–50 (1997).
• [16] KOSTEK B., Applying computational intelligence to musical acoustics, Archives of Acoustics, 32, 3, 617–629 (2007).
• [17] LARTILLOT O., MIR toolbox 1.0 User Guide, University of Jyvaskyla, Finland 2007.
• [18] LINDSAY A., HERRE J., MPEG-7 and MPEG-7 Audio – An Overview, 49, 7–8, 589–594 (2001).
• [19] PAWLAK Z., SKOWRON A., Rough sets and Boolean reasoning, Information Sciences, 177, 1, 41–73 (2007).
• [20] RABINER L., On the use of autocorrelation analysis for pitch detection, IEEE Trans. ASSP, 25, 24–33 (1977).
• [21] SZCZERBA M., CZY˙Z EWSKI A., Pitch Detection Enhancement Employing Music Prediction, J. Intelligent Information Systems, 24, 2–3, 223–251 (2005).
• [22] WUST O., CELM O., An MPEG-7 Database System and Application for Content-Based Management and Retrieval of Music, ISMIR 2004, Barcelona, Spain, October 10–14, 2004.
• [23] WITULSKI B., ŁUKASIK E., Multimedia presentation of musical instruments, Archives of Acoustics, 33, 1, 35–41 (2008).
• [24] WOŁKOWICZ J., KULKA Z., KEŠELJ V., n-gram-based approach to composer recognition, Archives of Acoustics, 33, 1, 43–55 (2008).
• [25] WÓJCIK J., KOSTEK B., Computational complexity of the algorithm creating hypermetric rhythmic hypotheses, Archives of Acoustics, 33, 1, 57–63 (2008).
• [26] VAPNIK V.N., Statistical Learning Theory, Wiley, New York 1998.
• [27] ZWAN P., KOSTEK B., System for Automatic Singing Voice Recognition, J. Audio Eng. Soc., 56, 9, 710–723 (2008).