Musical Information Dynamics as Models of Auditory Anticipation

Musical Information Dynamics as Models of Auditory Anticipation

Shlomo Dubnov (University of California in San Diego, CA)
Copyright: © 2011 |Pages: 27
DOI: 10.4018/978-1-61520-919-4.ch016
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This chapter investigates the modeling methods for musical cognition. The author explores possible relations between cognitive measures of musical structure and statistical signal properties that are revealed through information dynamics analysis. The addressed questions include: 1) description of music as an information source, 2) modeling of music–listener relations in terms of communication channel, 3) choice of musical features and dealing with their dependencies, 4) survey of different information measures for description of musical structure and measures of shared information between listener and the music, and 5) suggestion of new approach to characterization of listening experience in terms of different combinations of musical surface and structure expectancies.
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Structure Of This Chapter

After a brief introduction to the theories of expectancy in music and some historical as well as modern musicological and computational background,the authors will address the question of modeling music as an information source and listener as information receiver. From this approachthey will develop a model of listening that is based on mutual information between the past and the present called Information Rate (IR) (Dubnov, 2006; Dubnov et al. 2006). This model will be extended to include Predictive Information (PI) (Bialek et al.) and Predictive Information Rate (PIR) (Abdallah and Plumbley, 2009).

The authors introduce a new notion of Information Gap as a measure of the salience of a present musical segment (instance in time) with respect to future and past of a musical signal. This measure combines notions of predictive information with a notion of momentary forgetfulness, determining saliency of the present instance in terms of how detrimental forgetfulness is on the ability to make predictions. They will show that the information gap unites the three notions of information dynamics through simple algebraic relations. Next theywill consider application of IR for simple Markov chains (Cover and Thomas, 2006), and consider actual musical data from MIDI representation and cepstral audio features from recordings. Dealing with multiple features requires orthogonalization, which establishes the basis for vector IR.

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