Precision Apparatus Co Model E-200-C Signal Generator

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Table 6. Table 7 provides a comprehensive comparison of the affordances that define listening scenarios for Chua's Circuit and Swarm Chemistry sonifications. Metrics and quantitative measurements can be devised for many of the fields in Table 7 , including data that measures user interactions. The framework enables the impact comparison of each affordance across multiple listening scenario implementations.

Table 7. Listening scenario comparison of two sonification implementations using classification by affordances. The framework also identifies how a common function may be implemented in different subsystems. For example in both Chua's Circuit and Swarm Chemistry, attunement methods are applied to increase the reliability of sonfiying emergent behavior. In Chua's Circuit emergent behavior is tuned at transfer function TF 3 , using fiducial points located in the user interface. In Swarm Chemistry emergent behavior is tuned using automated feature recognition to control sonification design patterns, located at transfer function TF 1.

In summary, a sonfication framework identifies structural requirements and functional components to support comparisons of multiple implementations using measurement of attributes and related affordances. An extensible framework that can be used to establish symmetry for measuring and comparing system implementations, can further be applied as a reference model for measurement and comparison of user interactions and user experiences with diverse sonification systems.

Table 8 uses the sonification framework to summarize structures that align and variations in component implementation across three case studies. Structural symmetries identified in the framework can be used in the future to define human performance metrics, measuring and comparing user experience and productivity outcomes across multiple sonfication system. Table 8. Summary of case studies of emergent behavior sources, attunement techniques, tuning functions, and outcomes.

Emergent behaviors in non-linear dynamical systems are only partially predictable in best-case scenarios. A sonification framework facing probabilistic behavior intends to provide an affordance to observe all possible states of a system. The tension between indeterminate behavior and reproducibility poses a dilemma in terms of sonification objectives:. Reproduce experimental system behaviors as audible signatures;.

Render the salient features of emergent behaviors. Restated as a methodology problem: Emergent features may not provide a data model for linear coupling to predetermined audible features. Addressing this problem, two approaches for mapping data to sound have been presented.

One approach is to preselect sound to represent known features and use these audible signatures to define boundaries of unstable regions, aiding reliable exploration by close association. This is demonstrated in Case Study 1, the Chua's circuit sonificaiton using fiducial points to anchor the listener's interface with stable boundaries of unstable regions. Appendix 5 in Supplementary Material presents a related technique using attunement to compensate for hysteresis.

The other approach is to render sound to represent all data points and listen for patterns in the aggregate, applying a design that aims to ensure emergent data features will generate parallel emergent audible features. This is demonstrated in Case Study 2, the Swarm Chemistry sonification using a separate sound source to represent each of swarm agents. Neither approach offers a complete solution to the study of emergence. Case Study 3 demonstrates the application of sonification design patterns to provide strategies that aim to close the gap between these two approaches.

Modes of interactive exploration expand the conditions for interpreting sonfication by enhancing the listener's measure of temporal dynamics. As a listener explores a dynamical system, behaviors and sounds co-evolve. Dynamic navigation across control space provides a context to anchor sounds by association to movement and transformation. Heuristic listening involves attentive movement dynamics that complement the dynamics of control states and system responses. Modeling a listener provides criteria for measuring audible feature identification, both to assess target acquisition in known states of experimental systems, and to identify the salience of new features in emergent behaviors.

Interactive exploration optimizes for emergence, with exploration supported by attunement. Future work will study feasibility of sonification attunement applied to biological signals, anticipating a two-phase approach: 1 apply the framework to sonification of a biological reference model; 2 in the sonification framework replace the reference model with a biological data source. The relative instability of biological systems presents challenges. Measurements of biological information can experience signal fluctuations introduced from an experimental apparatus.

Noise may be introduced from data recording instrumentation and the surrounding environment. A biological system's states during a data acquisition trial period are unlikely to remain in a narrow mean that represents a constant value. Experimentally recorded biological data often requires disambiguation of information from noise.

In line with research practices that use models and simulations as references for comparison of noisy data, sonification attunement adopts models to generate audible reference features for comparison to data that exhibits unstable system behaviors. Physical constraints of biological systems may require specialized adaptation of the sonification framework.

Feasibility study of an experimental biological system is required prior to direct application of interactive sonification. Working from a simulation to an experimental system enables comparison of workflows—of the model and the physical system—to determine symmetry between the experimental design and the simulated control configuration. Instrumentation determines where and how attunement may be applied to extend an experimental workflow.

For biological experimental systems, real-time attunement feedback from tuning functions TF 2 to TF 3 may be challenged by physical limitations of interaction. Response characteristics of experimental biological systems may limit the capacity for real-time exploration.

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Time latency required to actuate state changes in a biological system may reduce the listener's sense of interaction. Establishing a parameterized exploration space for experimental acquisition of biological information requires system precision for inducing and measuring state changes. Attunement utilizes initial system exploration to identify salient features and boundaries of unstable regions. With biological information, the initial exploration process is qualified by the control parameters of the observing apparatus.

Constraints in implementation of experimental control space will qualify the initial exploration of the system, which is required to identify salient features of emergent behaviors. Detecting emergence will depend upon the instrumentation and the ability to identify fiducial points in experimental control space.

Finally, significant latency in experimental systems may impede interactive exploration required for heuristic listening. Sonification as data driven tone production requires interpretive and representative techniques for perceptual relevance, therefore generates design requirements.

Equally, sonification requires rigor to accurately interpret and represent data or systems with reproducibility, therefore generates scientific requirements. The sonification framework provides a reference model to generate requirements for implementation of interactive data processing coupled to sound generation. The framework is designed as a canonical model of interactive sonification, providing a small number of variables to represent the model, a simple tripartite structure based on symmetry of data flow, and a reusable template that can be applied to many systems.

The framework supports an attunement process to provide solutions for sonification of unpredictable data of non-linear and chaotic systems. The framework adopts a tripartite semiotic structure, which constructs the position of a sonification listener analogous to the position of a bioinformatician Figure The flexibility of the canonical model is demonstrated with two models that exhibit characteristics of biological systems, Chua's circuit and Swarm Chemistry.

Semiotic relationships of the sonification framework. In the first-order semiotic dyad, data-driven sounds signify experimental system states. In the second-order semiotic triad, data-driven sounds signify the listener's actions that bring about the system states. The listener's actions are identified as a second-order signified.

In attunement the two semiotic layers are concurrent. Terms in italics originate from Peirce's triadic semiotic model Peirce, The vertices of the semiotic triad align with the component subsystems of the sonification framework in Figure 3. To conclude, the sonification framework may be summarized as a set of requirements for design, implementation, and application of attunement. Architecture Requirements:. A controllable experimental data source that exhibits emergent behavior output as a digitized signal;. A serial relationship of three subsystems: an active observer controlling a dynamical system, the observed dynamical system generating experimental data, and a sound generator responding to the data to generate information acquired by listening;.

An interactive signal path that provides circular causality across the three subsystems, defining an explorable space for active listening;. Functional Requirements:. An interface to vary control parameters in real time for inducing changes in the experimental data source;. A sound generating engine with data driven control parameters;. Engineered distinction between data elicited from a signal and information interpretation of a received signal ;.

The design of the MEG II experiment

Three transfer functions tuning functions where conversion between data and information is performed for routing control signals from one subsystem to another;. Procedural Requirements:. For the dynamical system that is the experimental data source, enable observers to induce changes in the system states;. Measure the output signals of the experimental data source to identify salient features and emergent behaviors, and transmit this information across the framework;.

Given salient features in an output signal, enable observers to annotate the related system states by creating fiducial points in the user interface;. Given unstable behavior in the experimental system, identify control space boundaries of unstable regions and apply fiducial points in the user interface to mark the boundaries;. Apply data output by the experimental system to provide control of the sound generator, such that fiducial points have recognizable associated sounds;. User Experience Requirements:. For exploring an experimental system, provide gestalt orientation for listeners to learn system behaviors through multimodal experiences;.

For multimodal engagement, provide coupling through the experimental data source, between affordances of sound production and affordances of the user interface;. To generate a requisite variety of sounds, adopt sound synthesis design to generate a range of audible transformations of sounds with respect to a domain of measured changes in the experimental data;.

To connect outputs of data pattern recognition to a semiotic function in sound, design audible signatures for known salient features of emergent behaviors. Attunement implemented with pattern recognition provides a hybrid methodology to support reproducible observation, identification and feature discernment across multiple types of dynamic data sources using multiple types of sonification. The framework provides an efficient and extensible reference that integrates models of emergent behavior and models of a listener's attentive interaction with data.

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It may be applied to compare diverse sonification systems and applications, to identify common functions implemented in different subsystems, and to compare the impact of affordances across multiple implementations of listening scenarios. The author confirms being the sole contributor of this work and approved it for publication. The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

While varying in shapes and sizes, musical instruments commonly offer contact points and constrained space for performance navigation. The lesson to learn from this tradition is the optimization of instruments have been engineered with spatial abstraction, the distances and proportional relations for positioning physical elements; the optimization is highly targeted to achieve not only aesthetically sounding tones also physiologically coherent structure for the human body.

System coherence is reflected in human capacity to explore the instrument and its audible space with facility. The digital signal output of the simulation at a Extended sequences of voltage-control changes applied in parallel to the physical and simulated circuits maintain the two systems in common regions of phase space and produce audibly identical signals.

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Modulation of early auditory processing during selective listening to rapidly presented tones. Zhong, G. Experimental confirmation of chaos from Chua's circuit. Circuit Theor. Circuits for voltage tuning the parameters of chua's circuit: experimental application for musical signal generation. Keywords: sonification, listening, emergent behavior, interaction design, cognitive cycle, supramodal attention, biological information, media psychology.

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