Abstract
In this paper we compare the effectiveness of rhythm based signal segmentation technique with the traditional fixed length segmentation for music contents representation. We consider vocal regions, instrumental regions and chords which represent the harmony as different classes of music contents to be represented. The effectiveness of segmentation for music content representation is measured based on intra class feature stability, inter class high feature deviation and class modeling accuracy. Experimental results reveal music content representation is improved with rhythm based signal segmentation than with fixed length segmentation. With rhythm based segmentation, vocal and instrumental modeling accuracy and chord modeling accuracy are improved by 12% and 8% respectively.
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References
Goto, M.: An Audio-based Real-time Beat Tracking System for Music With or Without Drum-sounds. Journal of new Music Research 30(2), 159–171 (2001)
John, R.D., John, H.L., John, G.P.: Discrete-Time Processing of Speech Signals. IEEE Press, Los Alamitos (1999)
Kim, Y.E.: Singing Voice Analysis / Synthesis. PhD. Thesis, Massachusetts institute of Technology (September 2003)
Maddage, N.C., Xu, C.S., Kankanhalli, M.S., Shao, X.: Content-based Music Structure Analysis with the Applications to Music Semantic Understanding. In: ACM Multimedia Conference, New York (2004)
Maddage, N.C., Li, H., Kankanhalli, M.S.: Music Structure based Vector Space Retrieval. In: Proc. ACM SIGIR Conference (August 2006)
Rossing, T.D., Moore, F.R., Wheeler, P.A.: Science of Sound, 3rd edn. Addison-Wesley, Reading (2001)
Rudiments and Theory of Music, The associated board of the royal schools of music, 14 Bedford Square, London, WC1B 3JG (1949)
Aucouturier, J.-J., Sandler, M.: Finding Repeated Patterns in Acoustic Musical Signals: Applications for Audio Thumbnailing. AES 22nd International Conference on Virtual, Synthetic and Entertainment Audio, Finland (2002)
Brown, J.C.: Calculation of a Constant Q Spectral Transform. Journal of Acoustic Society of America 89(1) (1991)
Jourdain, R.: Music, The Brain, and Ecstasy: How Music Captures Our Imagination. HarperCollins press (1997)
Yoshioka, T., et al.: Automatic Chord Transcription with Concurrent Recognition of Chord Symbols and Boundaries. In: Proc. of 5th International Conference of Music Information Retrieval (ISMIR) (2004)
Nwe, T.L., Wang, Y.: Automatic Detection of Vocal Segments in Popular Songs. In: Proc. of 5th International Conference of Music Information Retrieval (ISMIR) (2004)
Ellis, D.P.W., Poliner, G.E.: Identifying ’cover songs’ with Chroma Features and Dynamic Programming Beat Tracking. In: ICASSP. Proc. International Conference on Acoustics, Speech, and Signal Processing (2006)
Duxburg, C., Sandler, M., Davies, M.: A Hybrid Approach to Musical Note Onset Detection. In: Proceedings of International Conference of Digital Audio Effects (DAFx), Hamburg, Germany ( September 2002)
Wang, Y., et al.: LyricAlly: Automatic Synchronization of Acoustic Music Signals and Textual Lyrics. In: ACM Multimedia Conference, New York (2004)
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© 2008 Springer-Verlag Berlin Heidelberg
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Maddage, N.C., Kankanhalli, M.S., Li, H. (2008). Effectiveness of Signal Segmentation for Music Content Representation. In: Satoh, S., Nack, F., Etoh, M. (eds) Advances in Multimedia Modeling. MMM 2008. Lecture Notes in Computer Science, vol 4903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77409-9_45
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DOI: https://doi.org/10.1007/978-3-540-77409-9_45
Publisher Name: Springer, Berlin, Heidelberg
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