The Entropic Envelope

Leonardo ◽  
2019 ◽  
Vol 52 (5) ◽  
pp. 455-460
Author(s):  
Hector Rodriguez
Keyword(s):  
Computational Model ◽  
Model Building ◽  
Image Sequences ◽  
Computation Model ◽  
Temporal Experience ◽  
Moving Image ◽  
Information Theoretic ◽  
Cinema Studies

This article introduces a computational model of the temporal experience of moving image sequences. The heart of the model, the concept of an entropic envelope, is based on information theoretic ideas. The concept is first described informally and then explained in a more mathematically precise manner. The article finally concludes with tentative reflections on the methodology of computation model building in cinema studies.

scholarly journals Information-theoretic Modeling of Perceived Musical Complexity

2019 ◽  
Vol 37 (2) ◽  
pp. 165-178
Author(s):  
Sarah A. Sauvé ◽  
Marcus T. Pearce
Keyword(s):  
Individual Differences ◽  
Computational Model ◽  
Information Content ◽  
Statistical Learning ◽  
Probabilistic Prediction ◽  
Information Theoretic ◽  
Musical Background ◽  
Musical Excerpts ◽  
Definition Of ◽  
Perceived Complexity

What makes a piece of music appear complex to a listener? This research extends previous work by Eerola (2016), examining information content generated by a computational model of auditory expectation (IDyOM) based on statistical learning and probabilistic prediction as an empirical definition of perceived musical complexity. We systematically manipulated the melody, rhythm, and harmony of short polyphonic musical excerpts using the model to ensure that these manipulations systematically varied information content in the intended direction. Complexity ratings collected from 28 participants were found to positively correlate most strongly with melodic and harmonic information content, which corresponded to descriptive musical features such as the proportion of out-of-key notes and tonal ambiguity. When individual differences were considered, these explained more variance than the manipulated predictors. Musical background was not a significant predictor of complexity ratings. The results support information content, as implemented by IDyOM, as an information-theoretic measure of complexity as well as extending IDyOM's range of applications to perceived complexity.

scholarly journals The Expectancy Dynamics of Anti-Tonal Twelve-Tone Rows: A Commentary and Reanalysis of von Hippel & Huron (2020)

2020 ◽  
Vol 15 (1-2) ◽  
pp. 128
Author(s):  
Niels Chr. Hansen
Keyword(s):  
Computational Model ◽  
Information Content ◽  
Music Cognition ◽  
Arnold Schoenberg ◽  
Anton Webern ◽  
Alban Berg ◽  
Twelve Tone ◽  
Information Theoretic ◽  
Information Theoretic Measures ◽  
High Uncertainty

This commentary provides two methodological expansions of von Hippel and Huron's (2020) empirical report on (anti-)tonality in twelve-tone rows by Arnold Schoenberg, Anton Webern, and Alban Berg. First, motivated by the theoretical importance of equality between all pitch classes in twelve-tone music, a full replication of their findings of "anti-tonality" in rows by Schoenberg and Webern is offered using a revised tonal fit measure which is not biased towards row-initial and row-final sub-segments. Second, motivated by a long-standing debate in music cognition research between distributional and sequential/dynamic tonality concepts, information-theoretic measures of entropy and information content are estimated by a computational model and pitted against distributional tonal fit measures. Whereas Schoenberg's rows are characterized by low distributional tonal fit, but do not strongly capitalize on tonal expectancy dynamics, Berg's rows exhibit tonal traits in terms of low unexpectedness, and Webern's rows achieve anti-tonal traits by combining high uncertainty and low unexpectedness through prominent use of the semitone interval. This analysis offers a complementary–and arguably more nuanced–picture of dodecaphonic compositional practice.

scholarly journals Decolonizing processes in film education

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jyoti Mistry
Keyword(s):  
Social Media ◽  
Social Justice ◽  
Case Studies ◽  
Image Sequences ◽  
Site Specific ◽  
Moving Image ◽  
Film Education ◽  
Social Media Platforms ◽  
Research Initiatives ◽  
Different Cultures

This paper considers what decolonizing film education might mean through a series of research initiatives undertaken across different cultures which explore social media platforms for creating moving image sequences. The paper attends to three factors in the current climate of education: the accessibility of the medium, its immediacy in dissemination, and the democratizing effect that these conditions have had on the medium of film. Working with these three conditions in contemporary film education, the case studies described include workshops that aimed to shift the curriculum from film canons to proposing the introduction of concepts. Furthermore, elided histories are explored through site-specific projects that show how decolonial processes allow these histories to be reclaimed in film practice, and for marginal subjectivities to be made visible. Finally, the proposal of decolonial processes seeks to work with creating opportunities for social and historical visibilities. The proposition is to work with film(ed) evidence as material connected to broader social justice issues that are expressed through aesthetic forms closely associated with decolonial processes and described as decolonial aesthesis.

Information Theoretic Analysis for Input Vector Selection in Black Box Modeling

2002 ◽  
Author(s):  
Brett Martin ◽  
Peter H. Meckl ◽  
Benjamin J. Zwissler
Keyword(s):  
Model Building ◽  
Mean Squared Error ◽  
Black Box ◽  
Input Vector ◽  
Data Sets ◽  
Theoretic Analysis ◽  
Validation Data ◽  
Information Theoretic ◽  
Past Data ◽  
The Relationship

When developing a black box model, the precise functional relationship between inputs and output is unknown. Engineers and scientists have turned to various regression tools in order to effectively capture the relationship based on past data observations. When modeling this data, however, it is important to only use inputs that provide information about the output. This paper presents a method of selecting the most informational input vectors for use in regression model building. This information-theoretic analysis for input vector selection requires only past data observations. Experimental results show that models built on the most informational input vectors produce less mean squared error on both training and validation data sets.

Advances in Info-Metrics

2020 ◽  
Keyword(s):  
Decision Making ◽  
Model Building ◽  
Building Blocks ◽  
Recent Book ◽  
Information Theoretic ◽  
Philosophical Foundations ◽  
Quantitative Skills ◽  
Recent Developments ◽  
Interdisciplinary Framework ◽  
General Meaning

Info-metrics is a framework for modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is an interdisciplinary framework situated at the intersection of information theory, statistical inference, and decision-making under uncertainty. In a recent book on the Foundations of Info-Metrics, Golan (OUP, 2018) provides the theoretical underpinning of info-metrics and the necessary tools and building blocks for using that framework. This volume complements Golan’s book and expands on the series of studies on the classical maximum entropy and Bayesian methods published in the different proceedings started with the seminal collection of Levine and Tribus (1979) and continuing annually. The objective of this volume is to expand the study of info-metrics, and information processing, across the sciences and to further explore the basis of information-theoretic inference and its mathematical and philosophical foundations. This volume is inherently interdisciplinary and applications oriented. It contains some of the recent developments in the field, as well as many new cross-disciplinary case studies and examples. The emphasis here is on the interrelationship between information and inference where we view the word ‘inference’ in its most general meaning – capturing all types of problem solving. That includes model building, theory creation, estimation, prediction, and decision making. The volume contains nineteen chapters in seven parts. Although chapters in each part are related, each chapter is self-contained; it provides the necessary tools for using the info-metrics framework for solving the problem confronted in that chapter. This volume is designed to be accessible for researchers, graduate students, and practitioners across the disciplines, requiring only some basic quantitative skills. The multidisciplinary nature and applications provide a hands-on experience for the reader.

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