A fascination for models derived from natural

organisation of organisms has a long history of

influence in the arts. This paper discusses emergence as

a complex behaviour and its manifestations in the sonic

domain. We address issues inherent in the use of

visual/spatial metaphors for sonic representation and

propose an approach based on sound interaction within

biological complex systems

Davis, Thomas

Rebelo, P.

English

A fascination for models derived from natural organisation of organisms has a long history of influence in the arts. This paper discusses emergence as a complex behaviour and its manifestations in the sonic domain. We address issues inherent in the use of visual/spatial metaphors for sonic representation and propose an approach based on sound interaction within biological complex systems. 1

Tom Davis

Pedro Rebelo

CiteSeerX

Hearing Emergence: Towards Sound-Based Self-Organisation

A fascination for models derived from natural organisation of organisms has a long history of influence in the arts. This paper discusses emergence as a complex behaviour and its manifestations in the sonic domain. We address issues inherent in the use of visual/spatial metaphors for sonic representation and propose an approach based on sound interaction within biological complex systems.</p

Davis, Tom

Rebelo, Pedro

Queen's University Belfast Research Portal

Hearing emergence:Towards sound-based self-organisation

University of Michigan Library Repository

HEARING EMERGENCE: TOWARDS SOUND-BASED SELF-ORGANISATION

Bournemouth University Research Online

HEARING EMERGENCE: TOWARDS SOUND BASEDSELF ORGANISATIONTom Davis Pedro RebeloSonic Arts Research CentreQueen’s University BelfastBelfast BT7 1NNEmail: tdavis01@qub.ac.uk Email: P.Rebelo@qub.ac.ukABSTRACTA fascination for models derived from naturalorganisation of organisms has a long history ofinfluence in the arts. This paper discusses emergence asa complex behaviour and its manifestations in the sonicdomain. We address issues inherent in the use ofvisual/spatial metaphors for sonic representation andpropose an approach based on sound interaction withinbiological complex systems.1. INTRODUCTIONIn recent years, there have been numerousimplementations of algorithmic systems that model thephenomena of emergent behaviour. In an attempt toreproduce the necessary conditions for emergentbehaviour to occur, these systems employ bottom-upstrategies through the definition of simple local rules forthe behaviour of agents. This type of characteristicallylocal behaviour has been paralleled with socialinteractions at play in live music performance,particularly improvisatory contexts [2], [8]. Musicalapplications have been widespread and include SwarmIntelligence for the selection of musical parameters [13],emergent behaviours for use in sound synthesis [9], andcomplex system modelling of human interaction in aperformance environment, [1] [2] [8]. Despite the largenumber of systems in existence that exploit theproperties of emergent behaviour there seems to be alack of systems that actually represent the perpetualnovelty and innovation of emergent behaviour within asonic context.2. MAPPING AND METAPHORThe systems mentioned above utilise emergentbehaviour based on examples in which emergentqualities become apparent through a user’s perception ofa graphical display. The properties of these graphicaldisplays are then mapped in different ways to try andproduce a sonic result that conveys their graphicalemergent qualities in audio form. This mapping processoften tries to derive a direct correlation between qualitiesof spatial position in the graphical model and a soniccharacteristic in the audio model [2] [13]. Thisseemingly arbitrary one-to-one mapping of spatial toaudio characteristics can be regarded as a sonification ofprimarily graphic systems rather than the design ofemergence in the sound domain.We propose that a re-thinking of the mappingprocess is essential in order to address the potential ofemergence as a sonic construct. John Holland, in hisbook ‘Emergence: from chaos to order’[6], suggests thatfor a model to be “successful” it should provide ametaphor for a system that enables us to see newconnections with, or add new meaning to, processes inthe already existing system. He goes on to say that‘deeper extended metaphors [should] allow for aprofound re-conception of the subject matter.’[6].Holland outlines three postulates for the source (original)and target (modeled) systems.1.  There is a source system with an establishedaura of facts and regularities.2. There is a target system with regularities andperhaps facts that are difficult to perceive orinterpret.3. There is a translation from source to target thatsuggests a means of transferring inferences forthe source into inferences for the target. [6].He suggests that if the source system hascommon underlying qualities with the target systemthen it is more likely for this transference of inferencesto occur and thus therefore more likely for the model tofacilitate a profound re-conception of the subject matter.This change in perception facilitated by the modelallows for a greater creative and imaginative explorationof the system space. The proposed relationship betweensource and target systems suggests a translation offunction rather than a mapping of results.Our research addresses systems that seek toexhibit emergence in the sonic domain by referring tocommon underlying qualities with the original nature-derived model. Expanding on the notion of“metaphorical systematicity”, Lakoff and Johnson notethat “to comprehend one aspect of a concept in terms ofanother… will necessarily hide other aspects of theconcept” [4]. In other words, a metaphorical relationshipis dependent on the identification of concepts that willcome into focus when considering two different systems.The model of the emergent system needs to act as ametaphor of the original system.Take for example the flocking motion apparentin a graphic representation generated by Craig Reynolds’Boids algorithm [11]. The Boids (flocking agents) inthis algorithm follow three simple rules:1. Collision Avoidance: avoid collisions with nearbyflockmates,2. Velocity Matching: attempt to match velocity withnearby flockmates,3. Flock Centering: attempt to stay close to nearbyflockmates.These three simple rules when appliedindividually to each agent in the system lead to theemergence of complex flocking behaviour such as thatfound in flocking birds, swarming bees and schoolingfish. When viewed in a graphical form the novelty andfascination of this system is derived from the changingposition of the many agents in two or three-dimensionalspace. Although the interactions occur between an agentand its neighbours on a local level it is only from themore removed perception of the scene as a whole that theseemingly complex flocking behaviour,  the constantreorganisation yet retention of shape and fascinatingnavigation of  available space becomes apparent. CraigReynolds described this system in his 1987 paper asfollows:“…it is simple in concept yet is so visuallycomplex, it seems randomly arrayed and yet ismagnificently synchronized… all evidence indicatesthat flock motion must be merely the aggregateresult of the actions of individual animals, eachacting solely on the basis of its own local perceptionof the world” [11].3. SPATIAL AND AURAL PERCEPTIONThe perception of emergent systems can be said to beintrinsically related to engagement. Whereas we are ableto easily detect visual patterns in behaviours such as theflocking of birds due to our ability to perceive allelements of the scene at once, it is unclear how the earmight be sensitive to emergent patterns that depend onthis overview. Although much has been written aboutthe differences between the eye and the ear in the contextof perception, the way we understand patterns throughsound is largely unknown.The notion of one’s body as the centre ofperception - Edmund Husserl’s zero point of orientation[3], is particularly relevant to sound. Whereas one can bea (visual) observer, treating the world in front of us as aspectacle viewed from a certain perspective, aural stimuliare mapped around our own body. This difference in roleraises issues of engagement and participation, whichsuggests that the ear needs to be treated differently fromthe eye. It is the trajectory performed by the ear, from asubtle tilt to the movement of the whole body, thatbecomes an active participant in the perception of anauditory scene. In the same way that behaviours such asflocking are better understood from a distance, we arguethat sonic emergence can only be perceived whenconsidering the listener as an agent of that verybehaviour. The ear does not act as a stethoscope,listening in from the outside, but rather as a participantin a space in which it takes the role of one, of manyagents.4.   SWARM LABThe prototype “Swarm Lab” focuses on the maintenanceof particular qualities found in the Boids system. Thebeauty in this model becomes apparent through theagents spatial / temporal relationships. In order to ensurea commonality of underlying mechanisms we opted for aliteral correlation of the spatial relationships in thismodel to those of a model of sound spatialization withina multi-loudspeaker space. This model retains all theaspects found in the graphical format representation ofthe flock and re-presents them in a sonic format. Thespatialisation of the flock or swarm within a spaceallows you to appreciate the same novelty andfascination in the spatial positioning of the sonic imageas found in the graphical representation. The listener ispositioned amongst the agents and hence is able toperceive the spatial distribution of the flock, the changefrom flocking behaviour to random behaviour, theposition of the flock in your space and the tightness orthe diffusion of the flocking.This model was designed for SARC’s SonicLaboratory using an eight-channel loudspeaker system atfloor level. This prototype is implemented usingMax/MSP primarily utilising the spatialisation objectvbap~ [15]. The interactions between swarm agents aregoverned by the Boids object created by Eric Singer[12], which is an implementation of Craig ReynoldsBoids algorithm for Max/MSP. Our implementationexplores the rendering of a sounding swarm byemploying Doppler, reverb and volume effects on top ofthe vbap~ spatialisation.This sonification of the flocking motion of theBoids objects adds new ways of creatively exploring theoutput produced by the Boids algorithm. The listenerbecomes an inhabiting agent rather than a voyeur as thesound envelopes the body in the environment moresignificantly than the graphical display. The spatialperception of the swarm is almost as strong, yet there isan emergence of new interactions not present in theoriginal graphical representation. The Boids themselvesnow interact on a audio signal level. Each agent emits itsown sound wave (a filtered noise impulse), which isseparately spatialised according to position; these soundwaves interact constructively or destructively dependingon their timing and amplitude. This leads to interestingphase effects and a change in sound corresponding tovariables such as, the speed of the swarm, the minimumdistance between Boids and the flocking strength of theswarm. The sonic exploration of these variables suggestsnew avenues in the creative exploration of the systemand helps promote types of perceptual engagement notpresent in the original model.5. SONIC FROGSA system currently under development addresses themodeling of a natural emergent listening environment asfound in a frog ecology. This is a system that considersa listener as an agent, relies on aural feedback, somelevel of social interaction, a system that is innatelyemergent on a sonic level, i.e. there is no high levelmapping between algorithmic and sound parameters.5.1 A Listening EcologyThrough a study of current research into the mating offrogs we have found that female frogs select a mate fromwithin the male frog chorus according to the temporaland spectral characteristics of their calls. Frogs have acomplex auditory system that is designed to help themrecognise and respond to calls of their own species. Theyhave a variety of different calls for such situations asmating, distress, release, warning, rain and definition ofterritory. The calls for different species are distinct intemporal and spectral characteristics. This helps the frogsrecognise calls of their own species from others within adense chorus [10]. The mating calls of frog species under study canbe characterised by four main parameters: dominant callfrequency (the frequency with the highest spectralintensity), pulse rate, call rate and call duration. Withdominant call frequency relating to frog size, pulse raterelating to the ambient temperature of the environment,for example the water temperature, and call rate and callduration relating to the preference of each individualanimal [7]. The characteristics that were found to havethe most effect on female choice of mate, were dominantcall frequency and call length [7]. It was found femalespreferred longer lower calls, the pitch of the calls havinga strong correlation to the body size of the males andthus their successfulness in mating. It has been found byWollerman that a “female frog could detect a singlemale’s calls mixed with the sounds of a chorus when theintensity of the calls was equal to that of the chorusnoise” [14]. Given that there is a 6dB fall off in theintensity of the signal with each doubling of distanceaway this means that for an average spaced chorus (0.08males per metre2 [14]), the female can only distinctly‘hear’ between three and five males at any one time.It has also been proposed that female frogsprefer ‘leading’ males, i.e. male calls that proceed othersin the group and that don’t overlap with other malescalls. Thus the male frogs within the chorus try not tocall at the same time as other frogs. Being subject to thesame auditory masking effect of the chorus as theirfemale counterparts they can only hear their nearestneighbours, but will actually change their call rate so asnot to temporally coincide with them [5].5.2 Formation of Rule SetWe took a distillation of these rules as a type set for oursystem. We proposed that males could only hear theirtwo nearest neighbours and that they would modulatetheir call rate so as not to correspond temporally to theirneighbours call. This was done using an implementationof a resettable oscillator as outlined in Greenfield et. al.[5]. This type of local interaction is characteristic ofemergent systems. The female states a preference for acertain male based on an “analysis” of the male’s calls.Due to the auditory masking effects of the chorus thefemale can only hear the nearest males, dependent onmale spacing and loudness of calls, and so has to explorethe sonic space created by the male’s interactions beforemaking a choice. Thus she is acting as a listening agentwithin an emergent environment. On choosing a male,the female and male ‘mate’ utilising a simple GeneticAlgorithm (GA) with a one point crossover. Theoutcome of this GA affects the parameters for granularsynthesis based on different recordings of frog calls.Thus a successful mating will result in a change of thefrog calls timbre and temporal characteristics. The femalealso caries her own set of characteristics, some of whichaffect her search criteria. The outcome of this GA processis at the centre of the exploration of the large-scaletemporal structure of the model and can be approached indifferent ways:- Only the males characteristics are affected bythe mating procedure, thus the female carries on hersearch for another mate unchanged. This tends to endearher to revisiting the same frog but as she is occasionallyattracted to others and with the probability of mutationset to just 2%, as time passes, there is a noticeablehomogenisation of the original wide diversity of calls.- A number of female frogs with differentcharacteristics and preferences competing to mate withthe males. The system under these conditions is lesslikely to stabalise on one sort of call but insteadfluctuates as the different females mate with the availablemales.- The mating process affects female’scharacteristics such that her preferences change during theGenetic Algorithm. This gives a greater variety ofpossible outcomes with more uncertain results and thus alikelihood of more interesting results of the developmentof frogs calls.This system has again been designed for aneight-channel loudspeaker system at SARC’s SonicLaboratory with the implementation of vbap~ [15] usedin the earlier Swarm Lab model. This implementationenables the listener to perceive the model from thefemale frog’s perspective and thus become engaged in theemergent world and act as a participant within the space.Figure 1. Flow chart of the Sonic frog model.6. CONCLUSIONThis paper addresses the notion of emergence byreferring to models intrinsically based on sonicinteraction. This has suggested possibilities beyondthose of high-level mapping between visually orientedmodels to sonic phenomena. The two models presentedhere attempt at incorporating non-linearities that arecharacteristic of natural behaviours. Events such as non-linear mutations or environmental changes must beintrinsic to metaphors of emergence and are central tothe design of temporal structures which are not onlybased on smoothed evolutionary patterns but alsointroduce elements of disruption and intervention.7.    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New York: Basic Books. 1999.[7] Howard, R.D. and Young, J.R., “Individualvariation in male vocal traits and female matingpreferences in Bufo americanus.” AnimalBehaviour, (55), pp. 1165-1179. 1998.[8] Impett, J. “A meta-trumpet(-er).” In Proceedingsof the 1994 ICMC. 1994.[9] Miranda, E., “At the Crossroads ofEvolutionary Computation and Music: Self-Programming Synthesizers, Swarm Orchestrasand the Origins of Melody.” EvolutionaryComputation, 12(2), pp. 137-158. 2004.[10]Olmsted, D.D., 28/8/2000, “Frog auditoryb e h a v i o r . ”  A v a i l a b l e  Online:http://www.neurocomputing.org/amphibian_neu   robiology/Frog_Auditory_Behavior/body_frog_   auditory_behavior.html  [02/2, 2005].[11]Reynolds, C.W., “Flocks, herds and schools: Adistributed behavioral model.” Proceedings ofthe 14th annual conference on ComputerGraphics and Interactive Techniques., , pp. 25-34. 1987.[12]Singer, E. “Boids for OSX” Available Online:http://www.ericsinger.com/cyclopsmax.html  .[03, 05][13]Spector, L. and Klein, J., “Complex AdaptiveMusic Systems in the BREVE SimulationEnvironment,” In: Workshop Proceedings of A-Life VIII, 2002, pp17-23. 2002.[14]Wollerman, L., “Acoustic interference limitscall detection in a Neotropical frog Hylaebraccata.” Anilmal Behaviour, 5 7 , pp. 529-536. 1999.[15]Pulkki.V “Virtual sound source positioningusing vector base amplitude panning.” Journalof the audio Engineering Society, 45(6) pp.456-466, June 1997.

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http://www.somasa.qub.ac.uk/~prebelo/index/research/davisrebelo05.pdf

Hearing Emergence: Towards Sound-Based Self-Organisation

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