Ulises: An Agent-Based System For Timbre Classification
Abstract The Sound and Music Computing (SMC) field has grown over the years and every time there are more conferences and specialized researchers in this area. The sub-field of Music Information Retrieval (MIR), one of the main research fields on SMC has focused on getting information from sound data. The most critical issue with regard to the human perception of sound is: what are the qualities of musical instrument sounds to perform recognition of its sound sources. There are four main sound dimensions: pitch, loudness, duration and timbre. The fourth dimension, timbre, is the most vague and complex dimension, a complex and high-level multidimensional property. Recognition of timbres is an area of high interest within MIR, being present in several papers state of the art on SMC. About Multi-Agent Systems (MAS), the term autonomous refers to the fact that the agents have their own existence, regardless of the existence of other agents, and are able to take own decisions without outside interference. Agents technology is particularly suitable for musical applications because of the possibility of associating a computational agent with the role of a singer or instrumentalist as can be seen in works state of art in SMC area. In this context, this paper proposes a agent-based approach to timbre recognition, focusing on the parallelization of the classification model. For this, we assign a method of recognition of timbres to different agents, where each agent is a specialized entity in a particular timbre, characteristic of a specific instrument, seeking a distributed solution for solving the timbre recognition problem.
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Beauchamp, J. W., 1982b. Synthesis by spectral amplitude and Brightness matching of analyzed musicalinstrument tones. Journal of the Audio Engineering Society, 30(6):396–406.
Bello, J. P., Daudet, L., Abdallah, S., Duxbury, C., Davies, M., and Sandler, M. B., 2005. A tutorial on onsetdetection in music signals. Speech and Audio Processing, IEEE Transactions on, 13(5):1035–1047.
Benade, A. H., 2012.Fundamentals of Musical Acoustics: Second. Courier Corporation.
von Bismarck, G., 1974. Timbre of steady sounds: A factorial investigation of its verbal attributes. Acta Acusticaunited with Acustica, 30(3):146–159.
Casey, M., Veltkamp, R., Goto, M., Leman, M., Rhodes, C., Slaney, M. et al., 2008. Content-based musicinformation retrieval: Current directions and future challenges. Proceedings of the IEEE, 96(4):668–696. - https://doi.org/10.1109/JPROC.2008.916370
Davis, S. B. and Mermelstein, P., 1980. Comparison of parametric representations for monosyllabic wordrecognition in continuously spoken sentences. Acoustics, Speech and Signal Processing, IEEE Transactionson, 28(4):357–366.
Devi, J. S., Srinivas, Y., and Krishna, N. M., 2012. A Study: Analysis of Music Features for Musical Instrument Recognition and Music Similarity Search. International Journal of Computer Science & Informatics (IJCSI),II(1):21–24.
Eronen, A. et al., 2001. Automatic musical instrument recognition. Memoire de DEA, Tempere University of Technology, page 178.
Grey, J. M., 1977. Multidimensional perceptual scaling of musical timbres. The Journal of the Acoustical Society of America, 61(5):1270–1277. - https://doi.org/10.1121/1.381428
Guide, M. U., 1998. The mathworks. Inc., Natick, MA, 5:333.
Handel, S., 1995. Timbre perception and auditory object identification. Hearing, pages 425–461. - https://doi.org/10.1016/B978-012505626-7/50014-5
Helmholtz, H. L. and Ellis, A. J., 1954. On the sensations of tone as a physiological basis for the theory of music (AJ Ellis, Trans.). New York: Dover.(Original work published in 1885).
Helmholtz, H. L. and Ellis, A. J., 2009. On the Sensations of Tone as a Physiological Basis for the Theory of Music. Cambridge University Press. - https://doi.org/10.1017/CBO9780511701801
Kitahara, T., 2007. Computational musical instrument recognition and its application to content-based musicinformation retrieval. Unpublished PhD Thesis, Kyoto University, Kyoto, Japan. Retrieved, 10(31):07.
Klapuri, A., 2004. Signal processing methods for the automatic transcription of music. Tampere University of Technology Finland.
Klingbeil, M. K., 2009.Spectral Analysis, Editing, and Resynthesis: Methods and Applications. Ph.D. thesis, Columbia University.
Lapp, D. R., 2003.The physics of music and musical instruments. Wright Center for Innovative ScienceEducation, Tufts University.
Lartillot, O. and Toiviainen, P., 2007. A Matlab toolbox for musical feature extraction from audio. In International Conference on Digital Audio Effects, pages 237–244.
Lartillot, O., Toiviainen, P., and Eerola, T., 2014. MIRtoolbox 1.6 User's Manual.
Lichte, W. H., 1941. Attributes of complex tones. Journal of Experimental Psychology, 28(6):455. - https://doi.org/10.1037/h0053526
Luce, D. and Clark Jr, M., 1967. Physical Correlates of Brass-Instrument Tones. The Journal of the Acoustical Society of America, 42(6):1232–1243. - https://doi.org/10.1121/1.1910710
Martin, K. D. and Kim, Y. E., 1998. 2pMU9. Musical instrument identification: A pattern-recognition approach. In Presented at the 136th meeting of the Acoustical Society of America. Citeseer.
Nordqvist, P., 2004. Sound classification in hearing instruments. Ph.D. thesis, KTH-S3.
Risset, J.-C. and Wessel, D. L., 1982. Exploration of timbre by analysis and synthesis. The psychology of music, 28. - https://doi.org/10.1016/B978-0-12-213562-0.50006-1
Roads, C., 1996. The computer music tutorial. MIT press.
Rumsey, F. and McCormick, T., 2012. Sound and recording: an introduction. CRC Press.
Sampaio, P., Tedesco, P., and Ramalho, G., 2005. Cinbalada: um laboratorio multiagente de geracao de ritmos de percussao. In Proceedings of the X Brazilian Symposium on Computer Music.
Sampaio, P. A., Ramalho, G., and Tedesco, P. A., 2008. CinBalada: Multiagent Rhythm Factory.J. Braz. Comp.Soc, 14(3):31–49. - https://doi.org/10.1007/BF03192563
Schoenberg, A., 1978. Theory of harmony. Univ of California Press.
Soraghan, S., 2014. Animating Timbre-A User Study. The 2014 IEEE International Conference on Systems, Man, and Cybernetics.
Stevens, S. S., Volkmann, J., and Newman, E. B., 1937. A scale for the measurement of the psychological magnitude pitch. The Journal of the Acoustical Society of America, 8(3):185–190. - https://doi.org/10.1121/1.1915893
Strong, W. J., 1963. Synthesis and recognition characteristics of wind instrument tones. Massachusetts Instituteof Technology.
Thomaz, L. F., 2009. A framework for implementing musical multiagent systems. 6th Sound and MusicComputing Conference, pages 119–124.
Wilensky, U., 1999. NetLogo.
Beauchamp, J. W., 1982b. Synthesis by spectral amplitude and Brightness matching of analyzed musicalinstrument tones. Journal of the Audio Engineering Society, 30(6):396–406.
Bello, J. P., Daudet, L., Abdallah, S., Duxbury, C., Davies, M., and Sandler, M. B., 2005. A tutorial on onsetdetection in music signals. Speech and Audio Processing, IEEE Transactions on, 13(5):1035–1047.
Benade, A. H., 2012.Fundamentals of Musical Acoustics: Second. Courier Corporation.
von Bismarck, G., 1974. Timbre of steady sounds: A factorial investigation of its verbal attributes. Acta Acusticaunited with Acustica, 30(3):146–159.
Casey, M., Veltkamp, R., Goto, M., Leman, M., Rhodes, C., Slaney, M. et al., 2008. Content-based musicinformation retrieval: Current directions and future challenges. Proceedings of the IEEE, 96(4):668–696. - https://doi.org/10.1109/JPROC.2008.916370
Davis, S. B. and Mermelstein, P., 1980. Comparison of parametric representations for monosyllabic wordrecognition in continuously spoken sentences. Acoustics, Speech and Signal Processing, IEEE Transactionson, 28(4):357–366.
Devi, J. S., Srinivas, Y., and Krishna, N. M., 2012. A Study: Analysis of Music Features for Musical Instrument Recognition and Music Similarity Search. International Journal of Computer Science & Informatics (IJCSI),II(1):21–24.
Eronen, A. et al., 2001. Automatic musical instrument recognition. Memoire de DEA, Tempere University of Technology, page 178.
Grey, J. M., 1977. Multidimensional perceptual scaling of musical timbres. The Journal of the Acoustical Society of America, 61(5):1270–1277. - https://doi.org/10.1121/1.381428
Guide, M. U., 1998. The mathworks. Inc., Natick, MA, 5:333.
Handel, S., 1995. Timbre perception and auditory object identification. Hearing, pages 425–461. - https://doi.org/10.1016/B978-012505626-7/50014-5
Helmholtz, H. L. and Ellis, A. J., 1954. On the sensations of tone as a physiological basis for the theory of music (AJ Ellis, Trans.). New York: Dover.(Original work published in 1885).
Helmholtz, H. L. and Ellis, A. J., 2009. On the Sensations of Tone as a Physiological Basis for the Theory of Music. Cambridge University Press. - https://doi.org/10.1017/CBO9780511701801
Kitahara, T., 2007. Computational musical instrument recognition and its application to content-based musicinformation retrieval. Unpublished PhD Thesis, Kyoto University, Kyoto, Japan. Retrieved, 10(31):07.
Klapuri, A., 2004. Signal processing methods for the automatic transcription of music. Tampere University of Technology Finland.
Klingbeil, M. K., 2009.Spectral Analysis, Editing, and Resynthesis: Methods and Applications. Ph.D. thesis, Columbia University.
Lapp, D. R., 2003.The physics of music and musical instruments. Wright Center for Innovative ScienceEducation, Tufts University.
Lartillot, O. and Toiviainen, P., 2007. A Matlab toolbox for musical feature extraction from audio. In International Conference on Digital Audio Effects, pages 237–244.
Lartillot, O., Toiviainen, P., and Eerola, T., 2014. MIRtoolbox 1.6 User's Manual.
Lichte, W. H., 1941. Attributes of complex tones. Journal of Experimental Psychology, 28(6):455. - https://doi.org/10.1037/h0053526
Luce, D. and Clark Jr, M., 1967. Physical Correlates of Brass-Instrument Tones. The Journal of the Acoustical Society of America, 42(6):1232–1243. - https://doi.org/10.1121/1.1910710
Martin, K. D. and Kim, Y. E., 1998. 2pMU9. Musical instrument identification: A pattern-recognition approach. In Presented at the 136th meeting of the Acoustical Society of America. Citeseer.
Nordqvist, P., 2004. Sound classification in hearing instruments. Ph.D. thesis, KTH-S3.
Risset, J.-C. and Wessel, D. L., 1982. Exploration of timbre by analysis and synthesis. The psychology of music, 28. - https://doi.org/10.1016/B978-0-12-213562-0.50006-1
Roads, C., 1996. The computer music tutorial. MIT press.
Rumsey, F. and McCormick, T., 2012. Sound and recording: an introduction. CRC Press.
Sampaio, P., Tedesco, P., and Ramalho, G., 2005. Cinbalada: um laboratorio multiagente de geracao de ritmos de percussao. In Proceedings of the X Brazilian Symposium on Computer Music.
Sampaio, P. A., Ramalho, G., and Tedesco, P. A., 2008. CinBalada: Multiagent Rhythm Factory.J. Braz. Comp.Soc, 14(3):31–49. - https://doi.org/10.1007/BF03192563
Schoenberg, A., 1978. Theory of harmony. Univ of California Press.
Soraghan, S., 2014. Animating Timbre-A User Study. The 2014 IEEE International Conference on Systems, Man, and Cybernetics.
Stevens, S. S., Volkmann, J., and Newman, E. B., 1937. A scale for the measurement of the psychological magnitude pitch. The Journal of the Acoustical Society of America, 8(3):185–190. - https://doi.org/10.1121/1.1915893
Strong, W. J., 1963. Synthesis and recognition characteristics of wind instrument tones. Massachusetts Instituteof Technology.
Thomaz, L. F., 2009. A framework for implementing musical multiagent systems. 6th Sound and MusicComputing Conference, pages 119–124.
Wilensky, U., 1999. NetLogo.
Teixeira, E. P., Goncalves, E. M. N., & Adamatti, D. F. (2018). Ulises: An Agent-Based System For Timbre Classification. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 7(1), 29–40. https://doi.org/10.14201/ADCAIJ2018712940
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- Eduardo Porto Teixeira, Eder M. N. Goncalves, Diana F. Adamatti, Ulises: A Agent-Based System For Timbre Classification , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 6 No. 2 (2017)
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