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"Human Supervision and Control in Engineering and Music" |
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Management of Musical DataDr. Bozena Kostek Sound & Vision Engineering Department, Technical University of Gdansk Narutowicza 11/12, 80-952 Gdansk, Poland bozenka@sound.eti.pg.gda.pl http://sound.eti.pg.gda.pl AbstractIn this overview some concepts concerning future perspectives of transdisciplinary research will be presented. There are many problems related to the management of musical data that are not solved up to now. These problems are being extensively developed within the Music Information Retrieval field now. Topics that should be addressed within the scope of this discussion, but not limited to, are as follows: the problem of automatically classifying musical instrument sounds and musical phrases/styles, music representation and indexing, estimating similarity of music using both perceptual and musical criteria, problems of recognizing music using audio or semantic description, building up musical databases, evaluation of MIR systems, intellectual property right issues, user interfaces, issues related to musical styles and genres, language modeling for music, user needs and expectations, auditory scene analysis, gesture control over musical work, etc. Some of these topic are covered by the MPEG 7 standardization process, which describe the multimedia content data that will support some degree of interpretation of the information meaning, ìwhich can be passed onto, or accessed by, a device or a computer code (MPEG-7)î. This aim of this overview is to provide some ideas as to prospective work which is needed in the domain of human supervision and control in engineering and music. They resulted from the experiments conducted for several years in the Sound & Vision Engineering Dept., Technical University of Gdansk, Poland. One can quote some exemplary problems that not solved up to now in the MIR domain: automatic search for musical melodies or instrument sounds, relationship between perceptually assessed and objectively measured sound parameters, methods of automatic assessment of sound attribute values and music quality, mimicking human way of composing, etc. One can refer to the rich literature related to these topics, examples of which are given in References. More detailed description on some of the mentioned topics is available through some of the cited authorís papers. As concluding remarks the author would like to mention that the prospective work is to apply soft computing methods to musical data present in databases as meta data. This means that both audio and higher level (i.e. semantic description) representation of musical data are included in feature vectors and will be processed at the same time. A query in this case can be defined as a set of keywords. This offers a very attractive field search for new techniques incorporating both signal and language engineering domains. Bello, J.P., Monti, G. & Sandler, M. (2000). Techniques for automatic music transcription, Proc. ISMIR. Brown, J.C. (1999). Computer indentification of musical instruments using pattern recognition with cepstral coefficients as features, J. Acoust. Soc. Am., 105, pp. 1933-1941. Coates, D. (1994). Representations of the MONK Harmonisation Systems, Proc. of Workshop held as part of AI-ED 93, M. Smith, A. Smith, A. Wiggins (Eds.), Edinburgh, Scotland, 25 August 1993, pp. 77-91, Springer Verlag, London. Eronen, A. & Klapuri, A. (2000). Musical Instrument Recognition Using Cepstral Coefficients and Temporal Features, Proc. IEEE Intern. Conference, ICASSPí2000. Herrera, P., Amatriain, X., Battle, E. & Serra, X. (2000). Towards Instrument Segmentation for Music Content Description: a Critical Review of Instrument Classification Techniques, Proc. Intern. Symposium on Music Information Retrieval. Holland, S. (1994). Learning About Harmony with Harmony Space: An Overview, Proc. of Workshop held as part of AI-ED 93, M. Smith, A. Smith, A. Wiggins (Eds.), Edinburgh, Scotland, 25 August 1993, pp. 24-40, Springer Verlag, London. http://www.meta-labs.com/mpeg-7-aud Kaminskyj, I. (2000). Multi-feature Musical Instrument Sound Classifier,
Kostek, B. & Czyzewski, A. (2001b) Representing Musical Instrument Sounds for Their Automatic Classification, J. Audio Eng. Society (in print). Kostek, B. (1998). Computer Based Recognition of Musical Phrases Using The Rough Set Approach, J. Information Sciences, 104, pp. 15-30. Kostek, B. (1999). 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. Papaodysseus, C., Roussopoulos, G., Fragoulis, D., Panagopoulos TH. & Alexiou C. (2001). A New Approach to the Automatic Recognition of Musical Recordings, J. Audio Eng. Soc., 49 (1/2). Smith, M., Smaill, A. & Wiggins, G.A. (Eds.). (1993). Music Education: An Artificial Intelligence Approach, Proc. of the World Conference on Artificial Intelligence in Education, Edinburgh. RAA (Recognition and Analysis of Audio. (2000). European project. http://www.iua.upf.es/mtg/raa |