Anoche pensé de pasada sobre algunas de las reglas no escritas de este blog (nadie lee las entradas largas, la gente es muy tierna por no pegarme con la zapatilla en la cabeza por subir tantas pajas mentales, después de tres años en esto todavía sigo sintiéndome vacío además de humillado tras confirmar la orden de publicación, etc.) aunque la que quizá más me gusta es aquella que dice “este blog se vuelve interesante cuando sale de sus zonas cómodas”. Más cosas que encontré ayer, en esta ocasión en libros, el primero sobre el cuerpo en la moderna cultura popular japonesa:
Okinawan-born techno/dance pop music star Amuro Namie represents one of the most popular types women have emulated, particularly in 1996–97, when she was the spokesmodel for the Takano Yuri Beauty Clinic. At that time, her long, light brown hair, makeup, slim body, and saucy outfits invoked a bar-hostess aesthetic rather than an unbaked teenager. Some observers even described Amuro as one of the new “hidden uglies” (busu kakushi), women who do not represent traditional beauty ideals but who are able to transform themselves into icons solely through fashion and cosmetics. Regardless of what critics made of her, young women copied her looks and her sassy attitude.3 Amuro is credited for initiating the “small face” fad (mikuro kei), which resulted in a new market for bogus face-slimming creams, packs, masks, and other goods. For example, the aesthetic salon Esute de Mirêdo oªered a Face Slim course (a treatment that supposedly makes a round face look more angular) of twelve treatments for $840. According to the manager of another aesthetic salon, “The reason for the small face trend is Amuro” (Yomiuri Shimbun 1997a). The brick-sized soles of Amuro’s shoes took hold, with sales of the retro style generating around $100 million in the late 1990s. Early versions of these platform shoes, boots, and sandals stood around four inches high, but by 1999 women wearing eight- or ten-inch heels could be seen advancing down the streets of Shibuya. Although simply called long boots or sandals by young women, the media dubbed these “Oiran shoes” after the high-ranking courtesans of the feudal period, called Oiran, who wore tall lacquered footwear for special promenades. Mega-platforms have been blamed for accidents, injuries, and even the death of a woman in 1999. In that instance, the victim fell and fractured her skull because she had just purchased the shoes and was not accustomed to wearing them.
Y estas dos entradas en un libro publicado el año pasado:
Sonic Algorithm
Steve Goodman
Contemporary sound art has come under the influence of digital simulations. These simulations are based on artificial life models, producing generative compositional systems derived from rules abstracted from actual processes occurring in nature. Yet taking these intersections of algorithms and art, divorced from a wider sonic field can be misleading. With their often arbitrary, metaphorical transcodings of processes in nature into musical notation, uncritical transpositions of artificial life into the artistic domain often neglect the qualitative, affective transformations that drive sonic culture. With care, however, we can learn much about the evolution of musical cultures from conceptions (both digital and memetic) of sonic algorithms—on the condition that we remember that software is never simply an internally closed system, but a catalytic network of relays connecting one analog domain to another. Here, the computing concept of the abstract machine attains a wider meaning, corresponding to the immanent forms that also pattern non- computational culture. For this reason, an analysis of the abstract culture of music requires the contextualization of digital forms within the contagious sonic field of memetic algorithms as they animate musicians, dancers and listeners.
An algorithm is a sequence of instructions performed in order to attain a finite solution to a problem or complete a task. Algorithms predate digital culture and are traceable in their origins to ancient mathematics. Whereas a computer program is the concretization or implementation of an assemblage of algorithms, the algorithm itself can be termed an abstract machine, a diagrammatic method that is programming language independent. Abstract machines are “mechanical contraptions [that] reach the level of abstract machines when they become mechanism- independent, that is, as soon as they can be thought of independently of their specific physical embodiments”1 thereby intensifying the powers of transmission, replication and proliferation. This quality of algorithms is crucial to software- based music, with key processes distilled to formalized equations that are generalizable, transferable, reversable, and applied. “Coupled with software (or mechanism or score or programme or diagram) that efficiently exploits these ideas, the abstract machine begins to propagate across the technological field, affecting the way people think about machines and eventually the world.”2 The affective power of the sonic algorithm is not limited to the morphology of music form. Leaking out of the sterile domain of the digital sound lab and across the audio- social field, these abstract machines traverse the host bodies of listeners, users, and dancers, producing movements and sensations, before migrating back to the vibratory substrate.
If, as Gottfried Leibniz proposed, all music is “unconscious counting,”3 then clearly, despite its recent popularity, algorithmic music composition cannot be considered the exclusive domain of computing. It should instead be placed in an historical context of experiments with, for example, out of phase tape recorders, where tape loops already constituted “social software organized to maximize the emergence of unanticipated musical matter.”4 As Michael Nyman has outlined, bottom- up approaches to musical composition take into account the context of composition and production as a system, and are “concerned with actions dependent on unpredictable conditions and on variables which arise from within the musical continuity.”5 Examples from the history of experimental music can be found in the oft- cited investigations of rule centered sonic composition processes in the exploration of randomness and chance, such as John Cage’s use of the I Ching, Terry Riley’s “In C,” Steve Reich’s “It’s Gonna Rain” and “Come Out,” Cornelius Cardew’s “The Great Learning,” Christian Wolff’s “Burdocks,” Frederic Rzewski’s “Spacecraft,” and Alvin Lucier’s “Vespers.”6 In this sense, as Kodwo Eshun argues, the “ideas of additive synthesis, loop structure, iteration and duplication are pre- digital. Far from new, the loop as sonic process predates the computer by decades. Synthesis precedes digitality by centuries.”7
Recent developments in software music have extended this earlier research into bottom- up compositional practice. Examples centering around the digital domain include software programs such as Supercollider, MaxMsp, Pure Data, Reactor and Camus,8 which deploy mathematical algorithms to simulate the conditions and dynamics of growth, complexity, emergence, and mutation of evolutionary algorithms and transcode them to musical parameters. The analysis of digital algorithms within the cultural domain of music is not limited to composition and creation. Recent Darwinian evolutionary musicology has attempted to simulate the conditions for the emergence and evolution of music styles as shifting ecologies of rules or conventions for music- making. These ecologies, it is claimed, while sustaining their organization, are also subject to change and constant adaption to the dynamic cultural environment. The suggestion in such studies is that the simulation of complexity usually found within biological systems may illuminate some of the more cryptic dynamics of musical systems.9 Here, music is understood as an adaptive system of sounds used by distributed agents (the members of some kind of collective; in this type of model, typically, none of the agents would have access to the others’ knowledge except what they hear) engaged in a sonic group encounter, whether as producers or listeners. Such a system would have no global supervision. Typical applications within this musicological context attempt to map the conditions of emergence for the origin and evolution10 of music cultures modeled as “artificially created worlds inhabited by virtual communities of musicians and listeners. Origins and evolution are studied here in the context of the cultural conventions that may emerge under a number of constraints, for example psychological, physiological and ecological.”11 Eduardo Miranda, despite issuing a cautionary note on the limitations of using biological models for the study of cultural phenomena,12 suggests that the results of such simulations may be of interest to composers keen to unearth new creation techniques. He asserts that artificial life should join acoustics, psychoacoustics, and artificial intelligence in the armory of the scientifically upgraded musician. According to Miranda, software models for evolutionary sound generation tend to be based on engines constructed around cellular automata or genetic algorithms.
Cellular automata were invented in the 1960s by von Neumann and Stanislaw Ulam as simulations of biological self- reproduction.13 Such models attempted to explain how an abstract machine could construct a copy of itself automatically. Cellular automata are commonly implemented as an ordered array or grid of variables termed cells. Each component cell of this matrix can be assigned values from a limited set of integers, and each value usually corresponds with a color. On screen, the functioning cellular automata is a mutating matrix of cells that edges forward in time at variable speed. The mutation of the pattern, while displaying some kind of global organization, is generated only through the implementation of a very limited system of rules that govern locally. Heavily influential to generative musicians such as Brian Eno, the most famous instantiation of cellular automata is John Conway’s Game of Life (1967). Game of Life has recently been implemented in the software system CAMUS, whereby the emergent behaviors of cellular automata are developed into a system that transposes the simple algorithmic processes into musical notation. The rules of the Game of Life are very simple. In the cellular grid, a square can be either dead or alive. With each generation, or step of the clock, the squares change status. A square with one or zero neighbors will die. A square with two neighbors will survive. A square with three neighbors becomes alive if not already, and a square with four or more neighbors will die from overcrowding. The focus of such generative music revolves around the emergent behavior of sonic lifeforms from their local neighborhood interactions, where no global tendencies are preprogrammed into the system.
As in the case of cellular automata and artifi cial neural networks, models based around genetic algorithms transpose a number of abstract models from biology, in particular the basic evolutionary biological processes identified in particular by Darwin14 and updated by Dawkins.15 These algorithms are often used to obtain and test optimal design or engineering results out of a wide range of combinatorial possibilities. Simulations so derived allow evolutionary systems to be iteratively modeled in the digital domain without the inefficiency and impracticality of more concrete trial and error methods. But, as Miranda points out, by abstracting from Darwinian processes such as natural selection based on fitness, crossover of genes, and mutation, “genetic algorithms go beyond standard combinatorial processing as they embody powerful mechanisms for targeting only potentially fruitful combinations.”16 In practice, genetic algorithms will usually be deployed iteratively (repeated until fitness tests are satisfied) on a set of binary codes that constitute the individuals in the population. Often this population of code will be randomly generated and can stand in for anything, such as musical notes. This presupposes some kind of codifycation schema involved in transposing the evolutionary dynamic into some kind of sonic notation, which, as Miranda points, out will usually seek to adopt the smallest possible “coding alphabet.” Typically each digit or cluster of digits will be cross- linked to a sonic quality such as pitch, or specific preset instruments as is typical in
genetic algorithms to evolve rhythmic patterns. In this case, the binary- string method is used to represent a series of equally spaced pulses whereby a note is articulated if the bit is switched on . . . and rests are made if the bit is switched off. The fitness test is based on a simple summing test; if the number of bits that are on is higher than a certain threshold, then the string meets the fitness test. High threshold values lead to rhythms with very high density up to the point where nearly all the pulses are switched on. Conversely, lower threshold settings tend to produce thinner textures, leading to complete silence.17
In summary, then, the development of artificial life techniques within music software culture aims to open the precoded possibilities of most applications to creative contingency.18 The scientific paradigm of artificial life marks a shift from a preoccupation with the composition of matter to a focus on the systemic interactions between the components out of which nature is under constant construction. Artificial life uses computers to simulate the functions of these actual interactions as patterns of information, investigating the global behaviors that arise from a multitude of local conjunctions and interactions. Instead of messy biochemical labs deployed to probe the makeup of chemicals, cells, etc., these evolutionary sonic algorithms instantiated in digital software take place in the artificial worlds of the CPU, hard disk, the computer screen, and speakers. However, with an extended definition of an abstract machine, sonic algorithms beyond artificial life must also describe the ways in which software- based music must always exceed the sterile and often aesthetically impoverished closed circuit of digital sound design. With non- software musics, such abstract machines leak out in analog sound waves, sometimes laying dormant in recorded media awaiting activation, sometimes mobilizing eardrums and bodies subject to coded numerical rules in the guise of rhythms and melodies. The broader notion of the abstract machine rewrites the connection between developments in software and a wider sonic culture via the zone of transduction between an abstract sonic pattern and its catalytic affects on a population. By exploring these noncomputational effects and the propagation of these sonic algorithms outside of digital space, software culture opens to the outside that was always within.
Notes
1. Manuel De Landa, War in the Age of Intelligent Machines, 142.
2. Kodwo Eshun, “An Unidentified Audio Event Arrives from the Post- Computer
Age,” in Jem Finer, ed., Longplayer, 11.
3. Gottfried W. Leibniz, Epistolae ad diversos, 240.
4. Eshun, “An Unidentified Audio Event Arrives from the Post- Computer Age,” 11.
5. Michael Nyman, Experimental Music: Cage & Beyond.
6. David Toop, “Growth and Complexity,” in Haunted Weather.
7. Eshun, “An Unidentified Audio Event Arrives from the Post- Computer Age,” 11.
8. Supercollider (http:// www.audiosynth.com/), MaxMsp (http:// www.cycling74.com/),
Pure Data (http: // puredata.info / ), Reactor (http: // www.native- instruments.com), Camus
(http:// website.lineone.net/~edandalex/ camus.htm).
9. See, for example, Peter Todd, “Simulating the Evolution of Musical Behavior,”
361–389.
10. Eduardo Miranda, Composing Music with Computers, 139–143, points to four mechanism
in origins and evolution useful for modeling musical systems:
a. transformation and selection (should preserve the information of the entity): improve
components of ecosystem evolution of music subject to psychophysiological constraints
rather than biological needs (i.e., survival); exceptions are bird song and mate
attraction
b. co- evolution: pushes the whole system (of transformations and selections) toward
greater complexity in a coordinated manner, e.g., musical styles co- evolve with music
instruments / technologies.
c. self- organization: coherence ingredients include (1) a set of possible variations, (2)
random fluctuations, and (3) a feedback mechanism.
d. level formation: formation of higher level compositional conventions, e.g., abstract
rules of rhythm e.g. metre and a sense of hierarchical functionality.
11. Miranda 2001, 119.
12. Miranda is particularly cautious of linear, progressive models of evolution:
Evolution is generally associated with the idea of the transition from an inferior species
to an superior one and this alleged superiority can often be measured by means of fairly
explicit and objective criteria: we believe, however, that this notion should be treated
with caution . . . with reference to prominently cultural phenomena, such as music, the
notion of evolution surely cannot have exactly the same connotations as it does in natural
history: biological and cultural evolution are therefore quite different domains. Cultural
evolution should be taken here as the transition from one state of affairs to another, not
necessarily associated with the notion of improvement. Cultural transition is normally
accompanied by an increase in the systems’ complexity, but note that “complex” is not a
synonym for “better.” (140)
13. E. F. Cood, Cellular Automata.
14. Charles Darwin, The Origin of Species, 1859.
15. Richard Dawkins, The Blind Watchmaker, 1986.
16. Eduardo Miranda, Composing Music with Computers, 131.
17. Ibid., 136.
18. See Peter Todd, “Simulating the Evolution of Musical Behavior,” and Eleonora
Bilotta, Pietro Pantano, and Valerio Talarico, “Synthetic Harmonies: An Approach to
Musical Semiosis by Means of Cellular Automata.”
Timeline (sonic)
Steve Goodman
A common feature of all time-based media, the timeline typically stratifies the on-screen workspace into a metric grid, adjustable in terms of temporal scale (hours/minutes/seconds/ musical bars or frames / scenes). With sonic timelines, zooming in and out, from the microsonic field of the sample to the macrosonic domain of a whole project, provides a frame for possible sonic shapes to be sculpted in time.
As an antidote to the digital philosophies of computer age, hype, many media philosophers have been reassessing the analog ground upon which digital technology is built. They are, questioning temporal ontologies, which emphasize the discreetness of matter via a spatialization of time (in the composition of the digital) in favor of a refocus on the continuity of duration. Typical objections to the ontology of the digital temporality share much with the philosophy of Henri Bergson. In Bergson’s philosophy of duration, he argues that the spatialization of time belies the “fundamental illusion” underpinning Western scientific thought. Bergson criticized the cinematographic error of Western scientific thought,1 which he describes as cutting continuous time into a series of discreet frames, separated from the temporal elaboration of movement, which is added afterward (via the action, in film, of the projector) through the perceptual effect of the persistence of vision. Yet sonic time plays an understated role in Bergson’s (imagistic) philosophy of time, being often taken as emblematic of his concept of duration as opposed to the cinematographic illusion of consciousness. In Time & Free Will he uses the liquidity of the sonic, “the notes of a tune, melting, so to speak, into one another” as exemplifying that aspect of duration that he terms “interpenetration.”2
The sequencer timeline is one manifestation of the digital coding of sound, which, while breeching Bergson’s spatialization of time taboo—an intensive sonic duration is visualized and therefore spatialized—has opened a range of possibilities in audiovisual production. The timeline traces, in Bergsonian terms, an illusory arrow of time, overcoding the terrain of the sequencing window from left to right. As with European musical notation’s inheritance from written text, digital audio software sequencers have inherited the habit of left-to right visual scanning. The timeline constitutes the spatialization of the clock into a horizontal time- coded strip that stretches from left to right across the screen, constituting the matrix of the sequencing window across which blocks of information are arranged. The sonic becomes a visualization in terms of a horizontally distributed waveform spectrograph, or sonic bricks. The temporal parts and the whole of a project are stretched out to cover an extensive space.
A temporal sequence of sounds suddenly occupies an area of the computer screen. What is opened up by this spatialization is the ease of temporal recombination. That marker of the transitory present, the cursor, and its ability to travel into the future and past (the right or left of the cursor) melts what appears, at least within the Bergsonian schema, to be the freezing of audio time into spatialized time stretches, instead of intensive durations. This arrangement facilitates nonlinear editing by establishing the possibility of moving to any point, constituting the key difference between nonlinear digital editing and analog fast forwarding and rewinding. The timeline pivoting around the cursor, marker of the transitory present, distributes the possible past (left of the cursor) and future (to the right of the cursor) of the project.
Aside from its improvement of the practicalities of editing and the manipulation of possibility, the digital encoding of sonic time has opened an additional sonic potential in terms of textural invention, a surplus value over analog processing. While the temporal frame of the timeline in digital applications makes much possible, a more fundamental temporal potential of sonic virtuality is locatable in the apparently un-Bergsonian realm of digital sampling, known as discrete time sampling.3 At a fundamental level, in its slicing of sonic matter into a multiplicity of freeze frames, digital samples treat analog continuity as bytes of numerically coded sonic time and intensity, grains which may or may not assume the consistency of tone continuity, the sonic equivalent of the persistence of vision.
Warning against the conceptual confusion of virtual potential with actual digital possibility, Brian Massumi notes that, despite the hype of the digital revolution, “sound is as analog as ever, at least on the playback end . . . It is only the coding of the sound that is digital. The digital is sandwiched between an analog disappearance into code at the recording and an analog appearance out of code at the listening end.”4 Yet, perhaps in the timestretching function a machinic surplus value or potential is opened in sonic time.
In contrast to the Bergsonian emphasis on continuity in duration, in the 1940s, the elementary granularity of sonic matter was noted by physicist Dennis Gabor, dividing time and frequency according to a grid known as the Gabor matrix. Prising open this quantum dimension of sonic time opened the field of potential, which much more recently became the timestretching tool within digital sound editing applications.5 The technique “elongates sounds without altering their pitch, demonstrates how the speed at which levels of acoustic intensity are digitally recorded (around 44,000 samples / second) means that a certain level of destratification is automatically accomplished. Since magnitudes (of acoustic intensity) are all that each sample bit contains, they can be manipulated so as to operate underneath the stratification of pitch / duration which depends on the differentiation of the relatively slow comprehensive temporality of cycles per second.”6
The technique referred to as time-stretching cuts the continuity between the duration of a sonic event and its frequency. In granular synthesis, discreet digital particles of time are modulated and sonic matter synthesized at the molecular level. In analog processing, to lower the pitch of a sound event adds to the length of the event. Slow down a record on a turntable for example, and a given word not only descends in pitch but takes a longer time to unfold. Or allocate a discreet sampled sound object to a zone of a midi keyboard; the difference between triggering the sample using one key, and moving to a key one octave down doubles the time of the sound, and halves its pitch. Timestretching, however, facilitates the manipulation of the length of a sonic event while maintaining its pitch, and vice versa. Timestretching, a digital manipulation process common to electronic music production is used particularly in the transposing of project elements between one tempo (or timeline) and another, fine tuning instruments, but also as a textural effect producing temporal perturbations in anomalous durations and cerated consistencies.
Notes
1. Henri Bergson, Creative Evolution, 322.
2. Henri Bergson, Time and Free Will: An Essay on the Immediate Data of Consciousness,
100.
3. Ken C. Pohlmann, Principles of Digital Audio, 21–22.
4. On the difference between the possible and potential (or virtual) see Brian Massumi,
“The Superiority of the Analog,” in Parables for the Virtual, 138.
5. Curtis Roads, Microsound, 57–60; and, Dennis Gabor, “Acoustical Quanta and the
Theory of Hearing.”
6. Robin Mackay, “Capitalism and Schizophrenia: Wildstyle in Effect,” 255.
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