Patterns of emergence and feedback topology in Ecos Study

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Glimpses of a performance ecosystem model

Article by Ricardo Thomasi

The Ecos Study is an experimental research that seeks musical structuring strategies in theories of emergence, finding fertile research ground in the audible ecosystem paradigm. A theoretical framework based on patterns of emergence was developed as guideline for performance and modelling, leading to structural thinking centred on the quality of interactions through which sounds take shape. We believe that the Echo Study can be developed complementarily with other theories in the field of electroacoustic music.

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tags: theory of emergence, musical structuring, audible ecosystem, acoustic feedback loop, live electronics

1. Introduction

Horacio Vaggione argues for a logic of emergence as a substitute for the traditional functional paradigm in musical composition [1]. He brings the idea of singularities as a form generator, borrowed from René Thom's theory [2], with two general archetypes: form as saliences – stratified states of interactions, discrete time events, form in a stable state –, and form as pregnancy – the process between saliences that makes them move from one state to another, the continuum, the force, the potency [3]. This perspective highlights the dual and inseparable aspect of sonorities when they are considered as emergent: the effect of the emergence of unique sounds – salience – and their unfolding hierarchy that highlights the process of sound morphology – pregnancy. The sounding traces resulting from the pregnancy-salience movement can be thought of as markers of structure and form. According to Impett, “we might posit another kind of structural time-point: events that change the dimensionality of the perceived musical discourse” [4], rather than a linear and sequential mode of event organisation. This is a very particular view of musical structuring that seeks to distinguish the relationships that exist between structures that underlie the surface of the emergence.

Looking through the prism of Simondon’s theory of individuation, we can find an operative field in these underlying relationships. In Simondon’s words, “what makes a being itself, different from all others, is neither its matter nor its form, but it is the operation by which its matter takes form in a system of internal resonance” [5]. For this reason, we believe that the structuring problem that occurs when dealing with emergence is centred on strategies for understanding the sound event through the interaction qualities that exist among the components that underlie its formation. This theoretical positioning leads us to the need to balance the micro and macro levels, acting on the low-level components, which exist before interactions, and at same time, analysing the structures that emerge from such interactions. In this way, it is possible to observe the connections among different layers of different nature through patterns of behaviour and constraints, allowing us to outline possibilities of relative control of the emergent structures, even if the middle levels remain as a black box. Since patterns of emergence can be analysed by discarding the ontological status and provide us with a generic view of the phenomenon [6], an analytical listening comes to foreground as a strong instrument to deal with complexity by enclosing the micro and macro levels at once. This approach may complement the existing listening models in the electroacoustic repertoire [7, 8, 9], since it is restricted to weak emergence, as we will discuss below.

Essentially, we believe that meaningful strategies to control emergent structures can be studied in models whose acoustic environment is part of the musical system. We have found in the Audible Ecosystem paradigm [10] fertile territory for experimentation and analysis whereby we are developing a model of feedback-loop instrumentalisation ¹ to explore the particularities of an ecosystemic perspective on musical performance [11]. Thus, the Ecos Study employs a MAX/MSP patch for feedback control and operation designed for live electronics performance ², which we call the Spatial Instrument [12], and a theoretical framework for analysis and modelling support based on patterns of emergence, which we will present in this paper.

2. Taxonomy of emergence and the feedback role in the Ecos Study

2.1 The scope problem when dealing with emergence

Stephen Jones argues that emergent structures are dependent on organising relations that are based on computational rules or operational behaviours in the physical world. Thus, “explanations for emergence require description of the relationships that link objects within some level of integration and between hierarchical levels of order, but we need a definition of order” [13]. In this case, the notion of order is often related with a process of ordering – therefore, a constant change in the hierarchical structure – rather than static, pre-existent and organised models [14]. An interesting property of the emergence phenomenon is the observation of an effect without an apparent cause. According to Fromm, “emergence is a creative, contingent and often unpredictable process: the stronger the emergence, the less predictable are the emergent properties, patterns and structures” [15]. It means that there are many types of causal relationships that integrate an emergent phenomenon, which leads us to think about a non-linear framework of causes. In other words, an action will not necessarily trigger a direct effect in the whole system – as a linear connection between cause and effect. Instead, it will trigger an unfolding chain of effects – where one effect becomes the cause of the next. Under the emergence prism, the relationships become our main operational interface.

This scenario drives the study of emergence to a relational perspective, which makes the level-by-level description or part-by-part segmentation of the system’s structure an almost impossible task. Since the emergent structure is a process of ordering, the hierarchical levels are also dependent on the observer’s perspective, turning the descriptive task into a scope and resolution problem. As Bar-Yam observes, “it is important to recognise that properties that we often associate with a part are actually relational properties and therefore are properties of the system rather than of the part” [16]. For this reason, first of all, it is important to distinguish at what scope the emerging phenomenon will be analysed and thus avoid a frequent conceptual imprecision in which every novelty or every organisational process is an emergence. Jochen Fromm proposes a classification model for emergent properties based on different types of feedback processes and causal structures, considering four types of emergence (see Table 1). First, the simple emergence, without top-down feedback, and that can be intentional or unintentional. Second, the weak emergence, that includes top-down feedback and can be stable or unstable. Third, emergence with multiple feedbacks, often with short-term positive feedback and long-term negative feedback, being able to generate adaptive emergence. The fourth and rarest one, strong and multilevel emergence, crosses the line of relevance, generating a whole new and independent world of interactions, rules, properties, like the emergence of a new life.

In Ecos Study, Fromm’s taxonomy of emergence works like an organology of the audible ecosystem. First, since it distinguishes the weak emergence, it helps us to define the scope of the experimental analysis below the cultural threshold, which belongs to the stronger types. It is pertinent since in the audible ecosystem we are not observing the emergent phenomenon from outside, but participating as an active component in the operative field. This leads us to naturally cross the four types of emergence, for example, when taking the emerging sound structures for a particular musical discourse. So, when modelling and performing, we can focus on more restricted and basic relationships, like the resonant frequencies and emergent sound spectra. Second, by shedding light on the emergent phenomenon through its causal relationships. The feedback processes enable us to figure out what types of emergence the system can produce and what types it cannot, even considering the complexity of non-linearity and causal asymmetry and especially on what concerns the sounding structures. Nevertheless, in Ecos Study we are exploring the influences of self-organisation present in Types I, II and IIIa.

We need only to find and reveal the hidden causal connections. In order to understand the phenomena in general, it is possible to start with a crude and coarse taxonomy. We can create a classification according to different feedback types and causal relationships [17].

Jochen Fromm

2.2 Taxonomy of emergence and illustrative comparisons

In the following paragraphs, we will briefly review Fromm's taxonomy, for illustrative purposes only, presenting possible examples of common situations in the musical field, should they be considered systems in themselves. We believe that a simple analysis is enough to outline the complexity of the typologies and their limitations, as well as how the taxonomy proposed by Fromm can help to outline models for musical performance.

Type 1a refers to a system that has passive and preprogrammed parts and that do not interact with one another, since there isn’t any feedback. There isn’t an emergence per se. What do exist are chains of reactions. For example, each part in a watch’s motor has a general function and, step by step, in synchronized movements, time is periodically counted as the watch’s hand moves. But none of its parts are aware of time, minutes or hours. If the same motor is used in a toy, its parts will work the same way. Also, if you take some parts out, the other parts will continue to work the way they were programmed to. The same happens with common digital instruments or digital audio workstations: they allow us to make music because we can handle their preprogrammed rules, predicting certain outcomes. In other words, their behaviour is always the same until something or someone changes their configuration. This is why Type 1a is called an intentional emergence. It does not form a complex system by itself.

On the other hand, Type 1b has a dependency relationship among low-level elements generated by local feedback properties. There is only a bottom-up, point-to-point feedforward relationship like dominoes, avalanches and waves propagation. However, these simple relationships can add complexity and dynamics, leading the system into an instability state that forces it to reorganise itself. We can also see this type of emergence in some cases of mixed tape music, where electronic sounds are perceived as extensions of the instrumental gestures and vice versa. The fixed electronic part, as a system itself, cannot change its state or adapt itself. It could, at most, trigger some preprogrammed transformation or evolve through a few cumulative processes, such as common audio effects – delays and reverbs, for instance ³.

In Type 2, weak emergence exhibits top-down feedback in addition to bottom-up causality, meaning that the low-level structures are also responsive to the resulting higher structures.

On the low microscopic level of the agent, many individual entities or agents interact locally with each other. This interaction results in a new, usually unpredicted pattern which appears at a higher level. On the high macroscopic level of the group or whole system, we notice unpredicted patterns, structures or properties - emergent phenomena - which are not directly specified in the interaction laws, and which in turn influence the low-level interactions of the entities via a feedback process [15].

Jochen Fromm

In this category, there are two ways of interaction: direct interaction, where agents interact with each other, and indirect interaction, where agents interact by changing the state of the environment. From these interactions, stable weak emergence may arise, usually with negative top-down feedback that imposes constraints on agents; and unstable weak emergence, characterised by positive feedbacks and the absence of self-regulation: the system grows to collapse and then reaches a new state of order, e.g. financial collapses and celebrity effect. Of course, there are mixtures, intermediate points, and transitions among these four subcategories. In music systems, top-down feedback appears in self-regulating models. A simple example, which does not result in emergent structures, is the audio compressor with automatic gain control inserted into a closed system: as the input signal increases the output decreases. In contrast, the Agostino Di Scipio’s Audible Ecosystem feedback control [19], that has a similar engine using envelope followers with two microphones for self-regulation, results in a system that is highly sensitive to acoustic perturbations, where sounding frequencies appear as macro level structures emerging from the organisation process. In this case, there are direct interactions between the input signal amplitudes and the algorithms themselves - in part, exhibiting Type 1b interactions - and indirect interactions when acoustic fluctuations and/or the sampling rate of the digital signal processing change the state of the system. In this sense, any arbitrary or random event can cause the system to reconfigure itself, thus generating complex relationships.

Type 3 refers to systems with multiple emergence and several simultaneous feedback models. Thus, the relationships between positive and negative feedback are similar to those of Type 2, but set in a more complex context. For instance, considering the example of mixed tape music discussed above, it jumps from Type 1 to Type 3 by including the feedback loop of human action-perception. In this sense, Di Scipio argues that,

In a typical ‘action-perception’ loop, each effect becomes a cause, each cause can disclose a number of effects causal to further effects. A whole ecology of action (Edgar Morin’s term) opens up in the specific domain, which requires agents to act responsibly in the present in order not to prejudicate future actions [20].

Agostino Di Scipio

Therefore, multiple feedback emergence is characterised by a combination of short-term positive feedback and long-term negative feedback. For example, the formation of biological patterns such as zebra stripes, which arise from the activation - positive feedback - and inhibition - negative feedback - of the cells that produce pigmentation. Type 3b is a higher level of emergence, adaptive emergence. For example, the elaboration of a new theory, in which old paradigms were the barriers that regulated and restricted the development of a specific thought. Also, environmental catastrophes that extinguish species force others to adapt. According to Fromm, "every mental barrier is like a small cognitive catastrophe, which results in a new insight and an avalanche of neural activities if an obstacle is suddenly overcome" [5]. In this sense, we can imagine the creative process itself as an ecosystem full of adaptive emergence [21]. Type 4, which refers to the strong and supervening emergence, is related to a vast scope ranging from cultural or social development - like the emergence of the audio culture and the feedback music trend - to the emergence of life. Faced with remarkable complexity, the analysis of Type 3b and Type 4 is far beyond our capabilities and the scope of this paper.

2.3 The feedback loop as generator of the metastability condition

The feedback loop between acoustic and digital environments constitutes the starting point of the audible ecosystem [10]. It is responsible for putting the components into a metastability condition, where they become responsive to each other. The level of the components’ responsiveness ⁴ creates the interactive basis that constitutes the system itself. The term metastability [22] refers to the condition of the system far from equilibrium, in which local instabilities may become the source of a new order. According to Prigogine, “non-equilibrium matter is much more sensitive to its environment than matter at equilibrium. I like to say that at equilibrium, matter is blind; far from equilibrium it may begin to see” [14].

Figure 1 shows the moment when the feedback frequency start to shape. A subtle increase in gain control causes the emergence of the first resonant frequency: an emergence from the physical qualities of the components as well as the qualities of their interactions. The system itself does not contain this frequency, except in the form of potential energy [5]; it does not have a tone generator that produces this frequency. In this case, the emergent resonant frequency also becomes an attractor component; a component that has the property to direct the evolution of the system [24]. Once the first frequency is controlled, it makes space for a second frequency and so on. For this reason, there is the emergence of a spectrum whose content depends on the acoustic, analogue and digital qualities that operate through the feedback loop. According to Prigogine,

When we drive a system far from equilibrium, the 'attractor' which dominates the behaviour of the system near equilibrium may become unstable, as a result of the flow of matter and energy which we direct at the system. Non-equilibrium becomes a source of order [14].

Ilya Prigogine

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The emergent sound from the feedback loop is directly related to a deeper causality structure that, in turn, appears in the Ecos Study as an operative field. A new metastable condition can be achieved at each state of the system, which enables us to identify and/or create new attractor components. Based on this process, we seek an instrumentalisation of the feedback loop through what we are calling feedback topology and feedback layers technique [25]. By acting on the feedback causality structure, it is also possible to predict how the components might interact. Such perspective suggests the emergence structural focus on the feedback-loop behaviour and system responsiveness, rather than on conceptions of pre-existing sound forms [8] or spatial movements influenced by equipment layouts [26] ⁵.

3. The micro-macro methodology in Ecos Study

It is complex to know exactly what is feeding back to the micro level and how it is related with the system’s organisation. One can say the acoustic fluctuations cause the digital algorithm to adapt itself. Actually, it is what we can see. But the top-down movement is deeper than that. The physical resistance of the equipment, the quality of the analogue-digital conversions, microphone and speakers’ position, and many other low-level components continuously inform and are informed by the macro structures. Since the feedback topology is intrinsically related with causal correlations, we do not need to describe the whole complexity, but understand the micro level interactions and how they appear at macro level. According to Fromm,

We can only generate complex self-organising systems with emergent properties in a goal-directed, straightforward way if we look at the microscopic level and the macroscopic level (for local and global patterns, properties and behaviours), examine causal dependencies across different scale and levels, and if we consider the congregation and composition of elements as well as their possible interactions and relations. A complex system can only be understood in terms of its parts and the interactions between them, if we consider static and dynamic aspects [27].

Jochen Fromm

Therefore, our methodology for modelling, experimentation and observation needs to consider micro and macro levels at once, combining the top-down and bottom-up approaches, enabling us to see “the static parts and dynamic interactions between them together with the macroscopic states of the system and the microscopic states of the constituents” [27]. Such a need meets Auyang’s proposition of Synthetic Microanalysis [28], where analysis and synthesis become a single process in which synthesis both precedes and follows analysis (see Figure 2). According to Auyang,

The approach neither stays on the top as holism demands nor makes a one-way journey from the bottom as micro reductionism stipulates. Instead, it combines the bottom-up and top-down views, making a round trip from the whole to its parts and back [28].

Sunny Y. Auyang

Jochen Fromm points out the disadvantage of the Synthetic Microanalysis method for system modelling because it is based on “human intelligence, creativity and experience, and needs constant manual intervention, observation and consideration” [27]. Also, its methodological weakness relies on the application of the scientific method by a single scientist. However, in Ecos Study we are not dealing with an artificial model, but with a real hybrid system that encompasses the acoustic and digital environments and the singularities of the artistic performance. In this sense, what Fromm points out as a drawback, we consider an advantage.⁶

In order to try to deal with the complexity of emergent processes in a feedback system, we resorted to a micro-macro methodology (see Figure 3) to support a) the modelling of low-level components envisioning the development of control strategies at micro-level, and b) the analysis of the macro-level structures through a listening method based on types of instabilities, as we will briefly discuss in the next two sections.

3.1 Feedback topology, causal correlations and the notion of control: the bottom-up

Considering that in complex thinking control is usually associated with goal-directedness, our notion of control assumes a broader conception that includes instability and randomness within a complex logic of organisation [29]. By acting in the systems’ feedback topology, we can change the whole causal structure and, consequently, affect the macroform. In other words, it is a sort of indirect control at micro-level that influences the system to reorganise itself and, therefore, to change the qualities of emergent structures. For this reason, causal relations are central in our effort to instrumentalise the feedback loop. According to Fromm,

Emergence is obviously related to hidden causal connections, causal relationships across different levels, or whole networks and braids of causal connections (upward, downward, sideward and mixed forms). Any scientific explanation should contain the clarification of causal connections [17].

Jochen Fromm

Causal relationships are probabilistic ones, because generally we don’t know all causes involved. For this reason, according to Pessoa Jr. [30], more suitable than cause is the idea of correlations of causes, the former being more a fetish or illusion than the latter. Pessoa Jr. argues about four possible conceptions of causal correlations ⁷ . The first is causality as a substantial relationship, when a body causes changes to another body while transferring part of its energy. It appears in the emergent sounds themselves, in phasing and harmonic beating effects and spectral fusion and fission, for example. The second type is causality by regularity, which evidences the linear concept of the occurrence of an event that causes the occurrence of another and, due to their regularity, both are taken as cause and effect. However, there is another possibility: it is when the cause-effect relationship triggers the occurrence of a third event, exhibiting a non-linear relationship. For instance, the use of periodic oscillators [31] which, through the regularity of the modulations of a given component, create an attractor point that can cause new emergent structures [12]. The third type is the counterfactual causation, which means cause in terms of conditionals if/then/else. It is well illustrative of digital programming languages, when one event causes another, provided it meets a certain requirement. But it is also related to the conditions acquired in the context of metastability, in which the components are faced with new behavioural conditions and attractor points. The fourth type is causation by manipulation, which seeks to understand the causal relationships by controlling one of the events. It may refer to the experimentation process itself, such as the performance in acoustic niches, the management of reflexive and absorbing surfaces through the room space, or even changing the feedback topology through the MAX/MSP patch in a live electronics fashion.

The rationale of these typologies allows us to observe interactions by their qualities and to distinguish when such causal correlations produce emergence and when they do not. According to Pessoa Jr.,

It is not enough to observe the statistical behaviour of the variables, it is also necessary to carry out experiments, which involve an intervention (control, manipulation) in the variables, fixing the values of certain causal elements to observe the generated effects [30].

Osvaldo Pessoa Jr.

Along with Fromm’s taxonomy, causal correlations typology supports the experimental field of Ecos Study as guidelines to define control strategies based on selection and production of attractor components and on the manipulation of the relational contexts. Considering the micro-level relationships, both typologies allow for a deeper insight into the quality of interactions (see figure 3).

3.2 Instabilities and bifurcations as listening support: the top-down

To balance the low-level actions with the emergent sound at the macro level we are developing an analytical listening method based on types of instabilities. It could be argued that this listening method is somehow analogous to Pierre Schaeffer's reduced listening [7]. However, we are attentive to the emergence which involves the sonorities as well as the other components that permeate the whole operative process that sustains the structure of the ecosystem. The instabilities that we will perceive through movements and sound qualities are only the surface, needing to be contrasted with the actions in the low-level components. In other words, the Ecos model demands listening to the structure of the interactions by drawing distinctions between types of dynamics in the sound; types of emergence far below the source-cause relationship in the spectromorphology sense, as mentioned earlier.

Although every emergence pattern is related to some sort of instability and nonlinearity, the opposite is not always true. At the very least, such behaviours are no guarantee of sufficiently relevant emergent structures. In fact, emergent structures are uncommon cases. According to Hooker,

In all systems it is true that the interacting components together create a dynamics that would not otherwise be present. When the outcome is surprising or unexpected or too complex to readily understand, scientists are apt to talk about emergent patterns. When the outcome is more complicated or subtle behaviour, and the dynamics is entirely internal to the system, it is said to be self-organised [32].

Cliff Hooker

This is one reason why audible ecosystems are such a special case for research. According to Di Scipio, “we construct a nature; we do not replicate or model a segment of extant nature” [33]. In other words, some of the inaccuracies in modelling dynamic systems that exhibit emergent structures become clearer given the simplicity of the audible ecosystem feedback structure. Instabilities are largely responsible for the organising processes in dynamical systems, and they can be regarded as the ontological core of central characteristics of emergence [34]. According to Schmidt, it is the “instability, and not the nonlinearity by itself, that makes the difference” [35].

Schmidt identifies three types of instability. First, static instability, based on the sensitive dependence of some initial conditions. It is a weak type of instability that differs from instabilities such as deterministic chaos - although in some cases it is a necessary condition for it. For instance, “a pen standing on its tip will eventually fall down to one side or another. Similarly, a ball on a roof ridge will roll down to one side or the other”, and also, “alternative trajectories from two nearby initial points separate and will never again become neighbours” [34]. So, at a certain point, the dynamics become less sensitive to time evolution. In this category lies the very emergence of the feedback loop resonant frequency and spectral unfolding through gain increase in Ecos Study (as shown in the Figure 1). At a certain point, some spectral bands become so dense or saturated that they lose sensitivity to each other, and the qualities of the feedback topology - the initial conditions - become barely apparent in the sonorities. From the performance perspective, one of the missions is not to let the ecosystem become insensitive.

The second type, dynamic instability, is associated with the deterministic chaos model. According to Schmidt, “for short time-scales, two trajectories may converge before they diverge again, and so on — an interplay of divergence and convergence among trajectories” [35]. It is characterised by a high sensitivity to time evolution, with dynamics also focused on trajectories and not only on initial conditions [36]. The Lorenz attractor is generally taken as an example of dynamic instability, which can also generate dynamics related to stronger emergence. In Ecos Study, emergent structures like harmonic beatings and pulsations generated by different superimposing feedback layers are part of the dynamic instability category.

The third type, structural instability, differs from the other two types because it does not refer to initial points or trajectories, but to the model structure itself. According to Schmidt, “if one alters the structure of a model (equation, law) slightly, the overall dynamics changes qualitatively” [35]. The structural instability is central in the Bifurcation Theory, and often associated with organization processes. Thus, we can understand the bifurcation concept as transition points. According to Hooker, we can distinguish two kinds of bifurcations, “a) a structural instability leading to a shift in dynamical form, and b) the subset of bifurcations that lead to the establishment of a new system level” [32]. In this sense, bifurcation refers to top-down constraints with a variety of intermediate strengths.

Figure 4 shows a simple flow chart of how the typology of instabilities has been thought in the Ecos Study. Static and dynamic types can drive bifurcations. If so, they can appear as changes in dynamics or as a new system level. If they do not have structural impact, the structural instability type, then they are just normal behavioral fluctuations that characterize the actual state of the emergence. Instead, structural instability takes the system to a new dimension.

In Figure 5 we can observe the two kinds of bifurcation. The segment A shows the shifts in dynamical form in function of gain parameter, characterizing a spectral expansion from the original sound structure. Although it is a new sound structure, they do not exhibit new top-down constraints; the spectral unfolding remains constrained to the first harmonic. So, it is a structural instability present in the emergent sound but without strong impact on the ecosystem structure. On the other hand, the segment B shows a bifurcation provoked by changing feedback topology and therefore generating new causal correlation chains and new top-down constraints. In other words, there is a new spectrum configuration as result of a new structural production condition of emergence.

4. The Ecos Study: preliminary results

The micro-macro methodology has helped to circumvent the challenge of distinguishing emergent structures from others. It revealed that the audible ecosystem structure is based on the resonant frequency profile of the feedback loop, drawing attention to the importance of preserving the acoustic responsiveness. Faced with this hypothesis, we have explored strategies to deal with the emergent structures, highlighting the structural role of some components looking forward applications in musical performance. The following examples were recorded with a handheld stereo recorder placed in the middle of the room, also capturing several external interferences in an attempt to bring the place of listening to the record ⁸.

4.1 The acoustic responsiveness

In the Ecos Study, the sensitivity to the acoustic fluctuations and the prominence of active listening are remarkable. In this example, there are four condenser microphones and four speakers of different types. MAX/MSP patch is only stabilizing the positive feedback, making the acoustic responsiveness of the Ecos model explicit. As I walk around with the handheld recorder, some of the morphologies that we hear are caused by interference from my body, acting as an absorbing surface, and by local properties around the microphone, which we call acoustic niches. At the end, when I lay down the recorder on the table and leave the room, the morphologies are attenuated not because the causality of emergent structures have changed, but mainly because we are listening from a static listening point.

We can better understand how the acoustic niches work in the second example. There is a feedback loop with just one microphone and one speaker. By changing the microphone’s position we change the feedback topology. By acting in the space around the microphone we also change the emergent sound, but we do not alter the feedback topology. In other words, there are two different categories of causal correlations, where in the first case we are dealing with causalities related to the global structure, whereas in the last one the manipulation causes changes only in local structures.

4.2 Sound spectra transitions and morphologies

As mentioned above, transitions may indicate bifurcation. However, different causal correlations may be involved in such processes, as the outcomes may or may not be an emergence. Considering the example below, we can observe spectral transitions that are achieved by changing the feedback topology, as well as multiple harmonic beatings that emerge from singular positioning of the gain structure through the topology. So, by manipulating low-level controls – the gain structure and in/out matrix in the MAX/MSP patch [12] – a substantial relationship is triggered provoking two kinds of emergent sound structures: the shifts of spectral configuration, showing a static instability profile, and the harmonic beatings with slightly dynamical instability profile. The interplay between these two events can drive a continuous evolving of the sound spectra.

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However, it is not the case of the second example. The descending glissando effect is achieved by continuous modulation a delay line inserted into the feedback loop. In the beginning, the delay modulation is explicit through the sound and it does not present an emergence at all – it configures Fromm’s Type 1a. But, after a few seconds, the initial glissando provokes the emergence of higher harmonics, since it stays in certain spectral bands long enough to resonate in the upper bands. So, the glissando effect become masked and the event evolve to an emergence Type 1b.

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4.3 Exploring the new metastability conditions

The acoustic-digital feedback loop is responsible for producing the metastability condition, making some components more sensitive to each other, and thus bringing out the audible ecosystem as a whole. However, it is possible to tension the metastability condition at each new state achieved. In this example, we are at a special place in the feedback topology where, through small changes in the control parameter, it is possible to modify the upper part of the spectrum. Apparently, the metastability state is a prominent space for musical performance.

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4.4 Working with feedback layers

As shown in the section 2, we can find multiple feedback chains participating in the formation of the emergent structure. It implies several types of feedback far beyond the acoustic feedback we are dealing with. But, creating parallel layers of feedback loop we can also increase the complexity of the ecosystem. In this sense, the feedback layers have appeared as a useful tool, because the acoustic environment works like a hub mixing the multiple layers and conserving the responsiveness of the fundamental loop, rather than directly modulating it as in the glissando’s case. For illustration, the fundamental loop was maintained in a very low dynamic, with just the first emergent harmonic – the lower one (see Figure 7). Then, we set up a single feedback layer with a five-seconds delay. There is no connection between the two loops except in the acoustic domain. The result is a dynamical instability that continuous transform the spectra content.

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4.5 Creating attractor components

In the Ecos model, resonant filters become strong attractors: since they are inserted into a positive feedback, their center frequency excites the correspondent frequency band of the emergent sound. So, it is possible to create harmonics artificially, but also to use the resonant filter to change a current harmonic if they resonate to each other. Figure 8 shows an excerpt where a resonant filter is manipulated to modulate the current harmonic. When it stops, the original emergent frequency returns.

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5. Final considerations

Throughout the Ecos Study, we realized that emergence theory can help us not only with system modelling, such as structuring and selecting controls, but also in developing a specific model of analytical listening for highly complex situations that can be useful in musical performance. We find in Fromm's taxonomy of emergence a strong theoretical basis, since it brings a perspective that considers feedback structures and causal correlations as the structural core. Thus, it allows a deeper insight into interactions through patterns of emergence that delineate important relationships between micro and macro levels. These become the operative field to perform with the audible ecosystem. In conclusion, we believe that the paradigm of audible ecosystems is a research territory capable of providing innovative models for electronic music performance as well as for structural thinking in contemporary music.

Acknowledgment

We acknowledge the financial support from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES) Finance Code 001, and Sala1 Studio for providing the physical space, equipment and technical support for the development of this research.

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Imprint

Author

Ricardo Thomasi

Issue

Date

31 January 2022

Footnotes

1 In this context, instrumentalisation means a set of control strategies that allow the performer to act on and through the feedback loop. In this sense, the human performer becomes part of the operative field.
2 The MAX/MSP patch for acoustic feedback control used in the Spatial Instrument is available for download here
3 As mentioned earlier, if we change the perspective and broaden the scope of what we call system, we will have more complex relationships - and possibly achieve stronger types of emergence. Taking the mixed tape music as an example, what emerges as musical gestures is much more than contrasts or similarities in timbre: the emergent structures jump to a high-level hierarchy if we consider the cognitive perspective. For example, Vaggione's stratification process by recording and selecting moments of specific encounters between instrument sounds and electronic sounds, from which higher order sound structures emerge [18]. In this case, we do not consider as a system just a limited causality channel - an attack-response movement, for example. There are multiple types of emergence grouped together, each of them with their own timeline evolution - in Simondon’s words, their own individuation [5]. We are clearly entering the cultural dimension here and, due to its notable complexity, it would be very challenging to objectively deal with the phenomenon without emptying the concept of emergence. Hence the importance for the Ecos Study to maintain the scope of the experiments with emergent structures on the weaker types.
4 This can also be understood as the quality of information that operates in the system producing transformations [23].
5 It is important to note that in the Ecos Model multi-channel spatialisation has a morphological role, as it is directly related to the feedback topology [25]
6 Interestingly, the same is true for other instances of the Ecos Study. The search for instabilities - when exploring the acoustic modes and singularities of the equipment, the microphone and speaker positions, and the very use of the positive feedback - is at the heart of the audible ecosystem and far from the goals of acoustic and audio engineering.
7 Causation and causality are synonymous here.
8 More information about the methodology can be found in this presentation of the Simpósio Internacional de Música Nova (SiMN), Brazil, 2021.