Chapter 18 Further Reading: Information Theory & Music

Primary Sources: Information Theory

Shannon, Claude E. "A Mathematical Theory of Communication." Bell System Technical Journal 27 (1948): 379–423 and 623–656. The original paper. The first 20 pages are accessible without advanced mathematics; the later sections become more technical. Reading the original is valuable for understanding exactly what Shannon claimed — and what he did not. Available freely online through Bell Labs historical archives and IEEE.

Shannon, Claude E., and Warren Weaver. The Mathematical Theory of Communication. University of Illinois Press, 1949. The accessible book-length version of Shannon's paper, with Weaver's non-technical introduction. The best starting point for readers without an engineering background.

Cover, Thomas M., and Joy A. Thomas. Elements of Information Theory. 2nd ed. Wiley, 2006. The standard graduate textbook on information theory. Chapters 1–3 (entropy, mutual information, channel capacity) are accessible to readers with basic probability knowledge. The definitive reference if you want to go deeper.

Information Theory and Music

Huron, David. Sweet Anticipation: Music and the Psychology of Expectation. MIT Press, 2006. The foundational text for ITPRA theory and the psychology of musical expectation. Rigorously argued, extensively evidenced, and beautifully written. Essential reading for anyone interested in the cognitive science of music. Chapters 2–5 develop the ITPRA model; chapters 6–14 apply it to specific musical phenomena.

Pearce, Marcus T. "The Construction and Evaluation of Statistical Models of Melodic Structure in Music Perception and Composition." Ph.D. thesis, City University London, 2005. (Subsequently published as various journal articles.) The most comprehensive computational study of melodic expectation, using information-theoretic measures. Pearce developed the IDyOM model, which predicts the information content (and hence the perceptual surprise) of individual notes in melodies. Technically demanding but groundbreaking.

Meyer, Leonard B. Emotion and Meaning in Music. University of Chicago Press, 1956. The foundational text for expectation-based theories of musical meaning, written before information theory was applied to music but deeply consistent with information-theoretic thinking. Meyer argues that musical meaning arises from the play of expectation and deviation. A classic of music aesthetics.

Narmour, Eugene. The Analysis and Cognition of Basic Melodic Structures. University of Chicago Press, 1990. Narmour's Implication-Realization model of melodic expectation, a computationally tractable theory of what makes melodic continuations expected or unexpected. Has been tested empirically and forms a precursor to information-theoretic approaches.

Computational Approaches

Conklin, Darrell, and Ian H. Witten. "Multiple Viewpoint Systems for Music Prediction." Journal of New Music Research 24, no. 1 (1995): 51–73. An early computational approach to musical prediction using multiple information-theoretic "viewpoints" (different representations of musical content). Accessible to readers with some computer science background.

Temperley, David. Music and Probability. MIT Press, 2007. A comprehensive treatment of probabilistic models of music, including harmonic analysis, melody, and key-finding. Technically accessible to readers with basic probability knowledge. Uses information-theoretic measures throughout.

Wiggins, Geraint A. "Semantic Gap?? Schemata?? Schemata!!." In Proceedings of the International Conference on Music Perception and Cognition (2010). A discussion of the "semantic gap" between acoustic features and musical meaning, highly relevant to the Spotify case study. Argues that closing this gap requires models of musical schema and expectation, not just acoustic signal analysis.

Spotify and Algorithmic Recommendation

Celma, Òscar. Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space. Springer, 2010. A comprehensive academic treatment of music recommendation systems, collaborative filtering, and their limitations. The best scholarly overview of the field.

Anderson, Chris. The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion, 2006. A popular account of how digital distribution (including music streaming) changes what gets heard. The "long tail" concept is directly relevant to how Spotify's algorithm interacts with musical diversity.

Spotify Research blog (research.spotify.com): Spotify publishes technical blog posts about their recommendation systems, audio analysis methods, and research findings. These are accessible to non-specialists and provide current information about how the system actually works.

Kolmogorov Complexity and Algorithmic Information Theory

Li, Ming, and Paul Vitányi. An Introduction to Kolmogorov Complexity and Its Applications. 3rd ed. Springer, 2008. The standard reference on Kolmogorov complexity. Chapters 1 and 2 provide an accessible introduction; later chapters are technically demanding. The section on applications to music cognition (Chapter 8) is directly relevant.

Schmidhuber, Jürgen. "Formal Theory of Creativity, Fun, and Intrinsic Motivation." IEEE Transactions on Autonomous Mental Development 2, no. 3 (2010): 230–247. An influential paper arguing that aesthetic pleasure is related to compression improvement — the brain rewards itself for discovering more compact descriptions of data. This is a Kolmogorov-complexity-based theory of aesthetic experience that directly applies to music.

Neuroscience of Musical Expectation

Koelsch, Stefan. Brain and Music. Wiley-Blackwell, 2012. A comprehensive review of the neuroscience of music, including extensive discussion of the ERAN and other neural responses to harmonic expectation. Technical but accessible to readers with basic neuroscience background.

Salimpoor, Valorie N., et al. "Anatomically Distinct Dopamine Release During Anticipation and Experience of Peak Emotion to Music." Nature Neuroscience 14 (2011): 257–262. The landmark neuroimaging study demonstrating dopamine release during musical "chills" (frisson), directly demonstrating the neural reward mechanism for musical expectation and tension-release cycles.

Zatorre, Robert J., and Valorie N. Salimpoor. "From Perception to Pleasure: Music and Its Neural Substrates." Proceedings of the National Academy of Sciences 110, Supplement 2 (2013): 10430–10437. An accessible review of the neuroscience of musical pleasure, covering prediction error, dopamine, and the role of learned expectations.

Cross-Cultural Information Theory

Savage, Patrick E., et al. "Statistical Universals Reveal the Structures and Functions of Human Music." Proceedings of the National Academy of Sciences 112, no. 29 (2015): 8987–8992. A cross-cultural study of musical universals using a large database of recordings from many cultures. Finds common statistical properties (including aspects related to entropy) while documenting substantial cultural variation.

Mehr, Samuel A., et al. "Universality and Diversity in Human Song." Science 366, no. 6468 (2019): eaax0868. A comprehensive cross-cultural study of music, including recordings from 315 societies. Relevant to the chapter's discussion of whether musical information structure is universal or culture-specific.