Abstract
The question of effectively communicating an artwork in a cultural context relies on joint understanding of certain creative conventions that are shared between an artist and his audience. Modeling of such cultural entrainment requires representation of a style that is specific to each genre, a task that depends in turn on particular compositional rules and aesthetic sensibilities of each culture. In this chapter we extend our previous research on machine learning of musical style into a broader approach of modeling aesthetic communication. The underlying cognitive assumption of our model is that listener’s experience of music is a process of actively seeking explanation by reducing the complexity of an incoming stream of sound through a process of approximation and prediction. Musical Information Dynamic is an analysis method that measures changes in the amount of information contents of musical signal over time. Motivated by semiotic analysis, we apply information dynamics analysis in order to measure the tradeoff between accuracy or level of approximation of a signal as captured by its basic units, and its overall information contents derived from its repetition structure. This approach allows us to formally analyze cultural communication in terms of aesthetic and poietic levels in paradigmatic analysis. Comparisons of flute recordings from Western and Far Eastern cultures show that optimal sensibilities to acoustic nuances that maximize the amount of information carried through larger structural elements in music are culture dependent.
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Notes
- 1.
The analysis package is LabROSA from Columbia University http://labrosa.ee.columbia.edu/.
References
Abdallah S, Plumbley M (2009) Information dynamics: patterns of expectation and surprise in the perception of music. Connect Sci 21(2-3):89–117
Assayag G, Dubnov, S (2004) Using factor oracles for machine improvisation. Soft Comput 8:1–7 (Springer Verlag)
Bal M, Bryson N (1991) Semiotics and art history. The Art Bulletin, vol 73, no 2, pp 174–208
Bense M (1969) Einfhrung in die informationstheoretische Asthetik. Grundlegung und Anwendung in der Texttheorie (Introduction to the Information-theoretical Aesthetics. Foundation and Application to the Text Theory) Rowohlt Taschenbuch Verlag
Collins T (2013) Discovery of repeated themes and sections. http://www.musicir.orgmirexwiki/2013:Discovery_of_Repeated_Themes_Sections. Accessed 4 May 2013
Collins T (2014) Sebastian bock, florian krebs, and gerhard widmer, bridging the audio symbolic gap: the discovery of repeated note content directly from polyphonic music audio. In: Audio engineering society conference: 53rd International conference: semantic audio. Audio Engineering Society
Cont A, Dubnov S, Assayag G, et al (2007) Guidage: a fast audio query guided assemblage. In: International computer music conference
Cont A, Dubnov S, Assayag G (2007) Anticipatory model of musical style imitation using collaborative and competitive reinforcement learning. In: Anticipatory behavior in adaptive learning systems. Springer, Berlin, Heidelberg, pp 285–306
Dubnov S, Assayag G, Lartillot O, Bejerano G (2003) Using machine-learning methods for musical style modeling. Comput Mag
Dubnov S (2006) Analysis of musical structure in audio and MIDI using information rate. In: Proceedings of international computer music conference (ICMC), New Orleans
Dubnov S, McAdams S, Reynolds R (2006) Structural and affective aspects of music from statistical audio signal analysis. J Am Soc Inf Sci Technol 57(11):15261536
Dubnov S, Assayag G, Cont A (2007) Audio Oracle: A New Algorithm for Fast Learning of Audio Structures. Proceedings of International Computer Music Conference (ICMC), 2007, Copenhagen, Denmark. ICMA
Dubnov S (2008) Unified view of prediction and repetition structure in audio signals with application to interest point detection. IEEE Trans Audio Speech Lang Process 16(2):327–337
Dubnov S, Assayag G, Cont A (2011) Audio oracle analysis of musical information rate. In: The fifth IEEE international conference on semantic computing, Palo Alto
Gottlieb J, Oudeyer PY, Lopes M, Baranes A (2013) Information seeking, curiosity, and attention: computational and neural mechanisms. Trends Cog Sci 17(11):585–593
Huron D (2006) Sweet Anticipation: Music and the Psychology of Expectation. MIT Press, 2006
Janssen B, Haas WB, Volk A, Kranenburg P (2013) Discovering repeated patterns in music: potentials, challenges, open questions. In: 10th international symposium on computer music multidisciplinary research. Laboratoire de Mecanique et d Acoustique
Lefebvre A., Lecroq T, Compror (2002), On-line lossless data compression with a factor oracle, Information Processing Letters 83 (2002) 1–6
Lefebvre A, Lecroq T, Alexandre J (2003) An improved algorithm for finding longest repeats with a modified factor oracle. J Automata Lang Combin 8(4):647–657
McFee B, Ellis DPW (2014) Analyzing song structure with spectral clustering. In: The 15th international society for music information retrieval conference, pp 405–410
Meyer LB (1956) Emotion and meaning in music. Chicago University Press, Chicago
Narmour E (1990) The analysis and cognition of basic melodic structures: the implication-realization model. University of Chicago Press
Nattiez J-J (1975) Fondements d’une Smiologie de la Musique. Union Gn-rale d’Editions, Paris
Nattiez J-J (1990) Music and discourse: towards a semiology of music. Princeton University Press, Princeton
Nieto O, Farbood M (2013) MIREX 2013: Discovering musical patterns using audio structural segmentation techniques. Music Information Retrieval Evaluation eXchange, Curitiba, Brazil
Nieto O, Farbood M (2014) Identifying polyphonic patterns from audio recordings using music segmentation techniques, In: The 15th international society for music information retrieval conference
Potter K, Wiggins GA, Pearce MT (2007) Towards greater objectivity in music theory: Information-dynamic analysis of minimalist music. Musicae Scientiae 11(2):295–322
Rigau J, Feixas M, Sbert M (2008) Informational aesthetics measures. IEEE Comput Graph Appl
Ruwet N (1972) Language, musique, posie. Editions du Seuil, Paris
Surges G, Dubnov S (2013) Feature selection and composition using pyoracle. In: The 9th artificial intelligence and interactive digital entertainment conference
Tillmann B, Kronland-Martinet R, Ystad S, Jensen K (eds) (2008) Music cognition: learning, perception, expectations: CMMR 2007, LNCS 4969, p 1133
Wang C, Dubnov S (2014) Guided music synthesis with variable markov oracle. In: The 3rd international workshop on musical metacreation, 10th Artificial intelligence and interactive digital entertainment conference
Wang C, Dubnov S (2015) Pattern discovery from audio recordings by variable markov oracle: a music information dynamics approach In: Proceedings of 40th IEEE internatinoal conference on acoustics, speech and signal processing, Brisbane
Wang C, Dubnov S (2015) The variable markov oracle: algorithms for human gesture applications. Multi Media IEEE 22(4):52–67
Acknowledgment
We would like to thank Mr. Cheng-I Wang from the Center for Research in Entertainment and Learning in UCSD for providing the VMO code and adopting the various VMO algorithms for this research, as well as his help with analysis of the musical examples.
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Dubnov, S., Burns, K., Kiyoki, Y. (2016). Information Sensibility as a Cultural Characteristic: Tuning to Sound Details for Aesthetic Experience. In: Cross-Cultural Multimedia Computing. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-42873-4_3
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