An approach to melodic segmentation and classification based on filtering with the Haar-wavelet

References

• Andén, J., & Mallat, S. (2011). Multiscale scattering for audio classification. Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), Utrecht, NL: ISMIR, pp. 657–662. Retrieved from http://ismir2011.ismir.net/papers/PS6-1.pdf
   Google Scholar
• Antoine, J.-P. (1999). Wavelet analysis: a new tool in physics. In J. C. van den Berg (Ed.), Wavelets in Physics (pp. 9–22). Cambridge: Cambridge University Press.
   Google Scholar
• Berger, J., Coifman, R., & Goldberg, M. (1994). Removing noise from music using local trigonometric bases and wavelet packets. Journal Audio Engineering Society, 42, 808–818.
   Web of Science ®Google Scholar
• Brown, M. (2005). Explaining tonality: schenkerian theory and beyond. Rochester, NY: University of Rochester Press.
   Google Scholar
• Cambouropoulos, E. (1997). Musical rhythm: a formal model for determining local boundaries, accents and metre in a melodic surface. In M. Leman (Ed.), Music, Gestalt and Computing: Studies in Cognitive and Systematic Musicology (pp. 277–293). Berlin: Springer.
   Google Scholar
• Cambouropoulos, E. (2001). The local boundary detection model (LBDM) and its application in the study of expressive timing. Proceedings of the International Computer Music Conference (pp. 17–22). San Francisco, CA: ICMA.
   Google Scholar
• Daubechies, I. (1996). Where do wavelets come from? A personal point of view. Proceedings of the IEEE, 84, 510–513.
   Google Scholar
• Daubechies, I., & Maes, S. (1996). A nonlinear squeezing of the continuous wavelet transform based on auditory nerve models. In A. Aldroubi, & M. Unser (Eds.), Wavelets in medicine and biology (pp. 527–546). Boca Raton, FL: CRC Press.
   Google Scholar
• de Boor, C. (1978). A practical guide to splines. New York: Springer-Verlag.
   Google Scholar
• Dobson, K., Yang, J., Whitney, N., Smart, K., & Rigstaa, P. (1996). A low complexity wavelet based audio compression method. Proceedings of the Data Compression Conference (DCC ‘96).
   Google Scholar
• Dreyfus, L. (1996). Bach and the Patterns of Invention. Cambridge, MA: Harvard University Press.
   Google Scholar
• Eerola, T., & Toiviainen, P. (2004). MIDI toolbox: MATLAB tools for music research. University of Jyväskylä. Retrieved from http://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/miditoolbox/
   Google Scholar
• Farge, M. (1992). Wavelet transforms and their applications to turbulence. Annual Review of Fluid Mechanics, 24, 395–457.
   Web of Science ®Google Scholar
• Forte, A., & Gilbert, S. (1982). Introduction to Schenkerian Analysis. New York: Norton.
   Google Scholar
• Grijp, L. P. (2008). Introduction. In L. P. Grijp & I. van Beersum (Eds.), Onder de groene linde. 163 verhalende liederen uit de mondelinge overlevering, opgenomen door Ate Doornbosch e.a./Under the green linden. 163 Dutch Ballads from the oral tradition recorded by Ate Doornbosch a.o. (Boek + 9 cd’s + 1 dvd), (pp. 18–27). Amsterdam/Hilversum: Meertens Instituut & Music and Words.
   Google Scholar
• Grimaldi, M., Cunningham, P., & Kokaram, A. (2003). A wavelet packet representation of audio signals for music genre classification using different ensemble and feature selection techniques. Proceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR ‘03), pp. 102–108.
   Google Scholar
• Haar, A. (1910). Zur Theorie der orthogonalen Funktionensyteme. Mathematische Annalen, 69, 331–371.
   Google Scholar
• Hillewaere, R., Manderick, B., & Conklin, D. (2009). Global feature versus event models for folk song classification. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009), (pp. 729–733). Kobe, Japan: ISMIR. Retrieved from http://ismir2009.ismir.net/proceedings/OS9-1.pdf
   Google Scholar
• Hillewaere, R., Manderick, B., & Conklin, D. (2012). String methods for folk music classification. Proceedings of the 13th International Society for Music Information Retrieval Conference (ISMIR 2012), Porto, Portugal, pp. 217–222. Retrieved from http://ismir2012.ismir.net/event/papers/217-ismir-2012.pdf
   Google Scholar
• Huron, D. (1996). The melodic arc in western folk songs. Computing in Musicology, 10, 3–23.
   Google Scholar
• Huron, D. (2006). Sweet anticipation: music and the psychology of expectation. Cambridge, MA: MIT Press.
   Google Scholar
• Jeon, W., Ma, C., & Ming Cheng, Y. (2009). An efficient signal-matching approach to melody indexing and search using continuous pitch contours and wavelets. Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009), Kobe, Japan, pp. 681–686. Retrieved from http://ismir2009.ismir.net/proceedings/PS4-18.pdf
   Google Scholar
• Jeon, W., & Ma, C. (2011). Efficient search of music pitch contours using wavelet transforms and segmented dynamic time warping. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), 2304–2307.
   Google Scholar
• Karmakar, A., Kumar, A., & Patney, R. (2011). Synthesis of an optimal wavelet based on auditory perception criterion. EURASIP Journal on Advances in Signal Processing, 2011:170927.
   Google Scholar
• Kay, K. N., Naselaris, T., Prenger, R. J., & Gallant, J. L. (2008, March 20). Identifying natural images from human brain activity. Nature, 452, 352–356. doi:10.1038/nature06713.
   Google Scholar
• Knopke, I., & Jürgensen, K. (2009). A system for identifying common melodic phrases in the masses of Palestrina. Journal of New Music Research, 38, 171–181.
   Web of Science ®Google Scholar
• Kurby, C. A., & Zacks, J. M. (2008). Segmentation in the perception and memory of events. Trends in Cognitive Sciences, 12, 72–79.
   PubMed Web of Science ®Google Scholar
• Lerdahl, F., & Jackendoff, R. (1983). A generative theory of tonal music. Cambridge, MA: MIT Press.
   Google Scholar
• Levitin, D. J. (2006). This is your brain on music: the science of a human obsession. New York, NY: Penguin.
   Google Scholar
• Mallat, S. (2009). A wavelet tour of signal processing: the sparse way (3rd ed.). Burlington, MA: Academic Press.
   Google Scholar
• Meredith, D. (2006). The ps13 pitch spelling algorithm. Journal of New Music Research, 35, 121–159.
   Web of Science ®Google Scholar
• Nixon, M. S., & Aguado, A. S. (2012). Feature extraction and image processing for computer vision (3rd ed.). Kidlington: Academic Press.
   Google Scholar
• Pinto, A. (2009). Indexing melodic sequences via wavelet transform. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME ’09). 882–885.
   Google Scholar
• Ponce de Léon, P. J., & Iñesta, J. M. (2004). Statistical description models for melody analysis and characterization. Proceedings of the International Computer Music Conference (ICMC 2004) (pp. 149–156). Miami, FL: ICMA.
   Google Scholar
• Schenker, H, (1935). Der freie Satz. (E. Oster, Trans. By as: Free Composition). New York, NY: Schirmer Books, 1979.
   Google Scholar
• Schmuckler, M. A. (1999). Testing models of melodic contour similarity. Music Perception, 16, 295–326.
   Web of Science ®Google Scholar
• Sinaga, F., Gunawan, T. S., & Ambikairajah, E. (2003). Wavelet packet based audio coding using temporal masking. The Fourth International Conference on Information, Communications and Signal Processing and Pacific-Rim Conference on Multimedia (ICICS-PCM ’03), 3, Singapore, pp. 1380–1383.
   Google Scholar
• Smith, L. M., & Honing, H. (2008). Time-frequency representation of musical rhythm by continuous wavelets. Journal of Mathematics and Music, 2, 81–97.
   Web of Science ®Google Scholar
• Srinivasan, P., & Jamieson, L. (1998). High quality audio compression using an adaptive wavelet packet decomposition and psychoacoustic modelling. IEEE Transactions on Signal Processing, 46, 1085–1093.
   Web of Science ®Google Scholar
• Stein, L. (1979). Structure and style: The study and analysis of musical forms. Expanded edition. Miami, FL: Summy-Birchard Music.
   Google Scholar
• Tenney, J., & Polansky, L. (1980). Temporal gestalt perception in music. Journal of Music Theory, 24, 205–241.
   Web of Science ®Google Scholar
• The Meertens Institute. (2012). Dutch song database. http://www.liederenbank.nl/index.php?lan=en
   Google Scholar
• Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79, 61–78.
   Web of Science ®Google Scholar
• Trainor, L. J., & Zatorre, R. J. (2009). The neurobiological basis of musical expectation. In S. Hallam, I. Cross, & M. Thaut (Eds.), The Oxford Handbook of Music Psychology (pp. 171–183). Oxford: Oxford University Press.
   Google Scholar
• Tsunoo, E., Ono, N., & Sagayama, S. (2009). Musical bass-line pattern clustering and its application to audio genre classification. Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009), Kobe, Japan, pp. 219–224. Retrieved from http://ismir2009.ismir.net/proceedings/PS2-5.pdf
   Google Scholar
• Tzanetakis, G., Essl, G., & Cook, P. (2001). Audio analysis using the discrete wavelet transform. Proceedings of WSES International Conference for Acoustics and Music: Theory and Applications (AMTA 2001), Skiathos, Greece. Retrieved from http://webhome.cs.uvic.ca/~gtzan/work/pubs/amta01gtzan.pdf
   Google Scholar
• van Kranenburg, P. (2010). A computational approach to content-based retrieval of folk song melodies (PhD thesis). Meertens Institute, Royal Netherlands Academy of Arts and Sciences (KNAW), NL. Retrieved from http://depot.knaw.nl/8400
   Google Scholar
• van Kranenburg, P., Volk, A., & Wiering, F. (2013). A comparison between global and local features for computational classification of folk song melodies. Journal of New Music Research, 42, 1–18.
   Web of Science ®Google Scholar
• Woody, N. A., & Brown, S. D. (2007). Selecting wavelet transform scales for multivariate classification. Journal of Chemometrics, 21, 357–363. doi:10.1002/cem.1060.
   Web of Science ®Google Scholar
• Yu, G., Mallat, S., & Bacry, E. (2008). Audio denoising by time-frequency block thresholding. IEEE Transactions on Signal processing, 56, 1830–1839.
   Web of Science ®Google Scholar
• Zhang, W., Shan, S., Qing, L., Chen, X., & Gao, W. (2009). Are Gabor phases really useless for face recognition? Pattern Analysis Applications, 12, 301–307.
   Web of Science ®Google Scholar
• Zhang, H., Zhang, B., Huang, W., & Tian, Q. (2005). Gabor wavelet associative memory for face recognition. IEEE Transactions on Neural Networks, 16, 275–278.
   PubMed Web of Science ®Google Scholar