Uma Abordagem Baseada em Agentes Para Um Sistema de Classificação de Timbres
Resumo
Este trabalho propõe uma abordagem baseada em agentes para o reconhecimento de timbres, com enfoque na autonomia dos agentes ao modelo de classificação de timbres. Para isto, atribui-se um método de reconhecimento de timbres a diferentes agentes, onde cada agente é uma entidade especialista em um determinado timbre, característico de um instrumento específico, visando uma solução ao problema de reconhecimento de timbres de forma distribuída.Referências
Beauchamp, J. W. (1982a). Data reduction and resynthesis of connected solo passages using frequency, amplitude, and “brightness” detection and the nonlinear synthesis technique. The Journal of the Acoustical Society of America, 71(S1):S101–S101.
Beauchamp, J. W. (1982b). Synthesis by spectral amplitude and” brightness” matching of analyzed musical instrument 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 onset detection 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.
Casey, M., Veltkamp, R., Goto, M., Leman, M., Rhodes, C., Slaney, M., et al. (2008). Content-based music information retrieval: Current directions and future challenges. Proceedings of the IEEE, 96(4):668–696.
Davis, S. B. and Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. Acoustics, Speech and Signal Processing, IEEE Transactions on, 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. IJCSI.
Eronen, A. et al. (2001). Automatic musical instrument recognition. Mémoire 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.
Guide, M. U. (1998). The mathworks. Inc., Natick, MA, 5:333.
Handel, S. (1995). Timbre perception and auditory object identification. Hearing, pages 425–461.
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.
Kitahara, T. (2007). Computational musical instrument recognition and its application to content-based music information 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. PhD thesis, Columbia University.
Lapp, D. R. (2003). The physics of music and musical instruments. Wright Center for Innovative Science Education, 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.
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.
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. PhD thesis, KTH-S3.
Risset, J.-C. and Wessel, D. L. (1982). Exploration of timbre by analysis and synthesis. The psychology of music, 28.
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 laboratório multiagente de geração 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.
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.
Strong, W. J. (1963). Synthesis and recognition characteristics of wind instrument tones. Massachusetts Institute of Technology.
Thomaz, L. F. (2009). A framework for implementing musical multiagent systems. 6th Sound and Music Computing Conference, pages 119–124.
von Bismarck, G. (1974). Timbre of steady sounds: A factorial investigation of its verbal attributes. Acta Acustica united with Acustica, 30(3):146–159.
Wilensky, U. (1999). Netlogo.
Beauchamp, J. W. (1982b). Synthesis by spectral amplitude and” brightness” matching of analyzed musical instrument 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 onset detection 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.
Casey, M., Veltkamp, R., Goto, M., Leman, M., Rhodes, C., Slaney, M., et al. (2008). Content-based music information retrieval: Current directions and future challenges. Proceedings of the IEEE, 96(4):668–696.
Davis, S. B. and Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. Acoustics, Speech and Signal Processing, IEEE Transactions on, 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. IJCSI.
Eronen, A. et al. (2001). Automatic musical instrument recognition. Mémoire 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.
Guide, M. U. (1998). The mathworks. Inc., Natick, MA, 5:333.
Handel, S. (1995). Timbre perception and auditory object identification. Hearing, pages 425–461.
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.
Kitahara, T. (2007). Computational musical instrument recognition and its application to content-based music information 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. PhD thesis, Columbia University.
Lapp, D. R. (2003). The physics of music and musical instruments. Wright Center for Innovative Science Education, 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.
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.
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. PhD thesis, KTH-S3.
Risset, J.-C. and Wessel, D. L. (1982). Exploration of timbre by analysis and synthesis. The psychology of music, 28.
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 laboratório multiagente de geração 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.
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.
Strong, W. J. (1963). Synthesis and recognition characteristics of wind instrument tones. Massachusetts Institute of Technology.
Thomaz, L. F. (2009). A framework for implementing musical multiagent systems. 6th Sound and Music Computing Conference, pages 119–124.
von Bismarck, G. (1974). Timbre of steady sounds: A factorial investigation of its verbal attributes. Acta Acustica united with Acustica, 30(3):146–159.
Wilensky, U. (1999). Netlogo.
Publicado
23/05/2016
Como Citar
TEIXEIRA, Eduardo P.; GONÇALVES, Eder M. N.; ADAMATTI, Diana F..
Uma Abordagem Baseada em Agentes Para Um Sistema de Classificação de Timbres. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 10. , 2016, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 23-33.
ISSN 2326-5434.
DOI: https://doi.org/10.5753/wesaac.2016.33203.
