Abstract
We propose a novel approach to detect semantic regions (pure vocals, pure instrumental and instrumental mixed vocals) in acoustic music signals. The acoustic music signal is first segmented at the beat level based on our proposed rhythm tracking algorithm. Then for each segment Cepstral coefficients are extracted from the Octave Scale to characterize music content. Finally, a hierarchical classification method is proposed to detect semantic regions. Different from previous methods, our proposed approach fully considers the music knowledge in segmenting and detecting the semantic regions in music signals. Experimental results illustrate that over 80% accuracy is achieved for semantic region detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Berenzweig, A.L., Ellis, D.P.W.: Location singing voice segments within music signals. In: Proc. IEEE WASPAA, Paltz, New York (October 2001)
Bilmes, J.: A gentle tutorial on the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Technical Report ICSI-TR-97-021, University of Berkeley (1998)
Collobert, R., Bengio, S.: SVMTorch: Support Vector Machines for Large-Scale Regression Problems. Journal of Machine Learning Research 1, 143–160 (2001)
Deller, J.R., Hansen, J.H.L., Proakis, H.J.G.: Discrete-Time Processing of Speech Signals. IEEE Press, Los Alamitos (2000)
Duxburg, C., Sandler, M., Davies, M.: A Hybrid Approach to Musical Note Onset Detection. In: Proc. of DAFX, Germany (2002)
Eronen, A., Klapuri, A.: Musical Instrument Recognition Using Cepstral Cofficients and Temporal Features. In: Proc. of ICASSP, Istanbul (2000)
Fujinaga, I.: Machine Recognition of Timbre Using Steady-state Tone of Acoustic Musical Instruments. In: Proc. of ICMC, pp. 207–210 (1998)
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)
Goto, M., Muraoka, Y.: A Beat Tracking System for Acoustic Signals of Music. In: Proc. of the Second ACM Intl. Conf. on Multimedia, pp. 365–372 (1994)
Jiang, D., et al.: Music Type Classification by Spectral Contrast Fatures. In: Proc. of ICME, Switzerland (2002)
Maddage, N.C., Xu, C., Wang, Y.: A SVM-Based Classification Approach to Musical Audio. In: Proc of ISMIR, Maryland, USA (2003)
Kim, Y.K., Brian, W.: Singer Identification in Popular Music Recordings Using Voice Coding Features. In: Proc. of ISMIR, France (2002)
Saitou, T., Unoki, M., Akagi, M.: Extraction of F0 Dynamic Characteristics and Developments of F0 Control Model in Singing Voice. In: Proc. of ICAD, Japan (2002)
Scheirer, E.D.: Tempo and Beat Analysis of Acoustic Musical Signals. JASA 103(1) (1998)
Sundberg, J.: The Science of the Singing Voice. Northern Illinois University Press, Dekalb (1987)
Young, S., et al.: The HTK Book. Version 3.2 (2002)
Zhang, T.: Automatic singer identification. In: Proc.of ICME, Maryland, USA (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Maddage, N.C., Xu, C., Shenoy, A., Wang, Y. (2004). Semantic Region Detection in Acoustic Music Signals. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30542-2_108
Download citation
DOI: https://doi.org/10.1007/978-3-540-30542-2_108
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23977-2
Online ISBN: 978-3-540-30542-2
eBook Packages: Computer ScienceComputer Science (R0)