polyPhonic
mode & musings
_polyPhonic _dhiyaMuhammad _2011 _singapore _introduction
INTRODUCTION
polyPhonic is an approach to represent sound as an object. The project delves into the study of sounds’ data and modulates it as physical objects. The polygonal structures are derived from a sounds’ spectral analysis whose points are simplified in two-dimensional space. A Delaunay Triangulation algorithm is adapted to create a threedimensional mesh to establish form and structure. polyPhonic is adapted in the Lasalle Show, structures are used to distinguish the three different specialism coursed for the BA Degree level and throughout the Design faculty.
_polyPhonic _dhiyaMuhammad _2011 _singapore _maschine
AUDIO USED
Various – Clicks_+_Cuts Label:Mille Plateaux, Mille Plateaux Catalog#:mp79, _mp 79_ Format:2 × CD, Compilation Country:Germany Released:Jan 2000 Genre:Electronic Style: Abstract, IDM, Experimental
Title: Maschine Duration: 08:04 Artist: Ester Brinkman, Thomas Brinkman
The audio used in this project is a song no.1 on disc 2 called MASCHINE by Ester Brinkman & Thomas Brinkman in Alva Noto’s, a double CD compilation titled CLICKS_+_CUTS. The song’s duration is timed at 08mins 04secs. Ester Brinkmann’s song “Maschine” is featuring looped vocal sample spoken by Blixa Bargeld from Einstürzende Neubauten record - “Die Hamletmaschine” (from about 25:42-25:49). That sample is “Ich will eine Maschine sein. Arme zu greifen Beine zu gehn kein Schmerz kein Gedanke.” translated as “I WANT TO BE A MACHINE,ACCESS TO ARMS,LEGS TO GO,NO PAIN,NO PLANS”.
Ich will eine Maschine sein. Arme zu greifen Beine zu gehn kein Schmerz kein Gedanke
_polyPhonic _dhiyaMuhammad _2011 _singapore _softwares
COMPUTATIONAL PLATFORMS
To achieve the end outcome, several software platforms are adapted. AUDACITY is used to distinguish the audio’s spectral and frequency analysis with it’s plot spectrum function from the chosen audio’s data. ADOBE ILLUSTRATOR is used to draw, simplify and create the points from the audio’s spectral analysis. PROCESSING, a java based programming platform is used to run a written Delaunay Triangulation algorithm sketch overlaying the simplified spectral analysis.
AUDACITY Audacity is a free, open source software for recording and editing sounds. It is available for Mac OS X, Microsoft Windows, GNU/Linux, and other operating systems. www.audacity.sourceforge.net
GOOGLE SKETCHUP is then used to extrude the two-dimensional mesh created from the Delaunay Triangulation algorithm into a three-dimensional structure. A specific UNWRAP plug-in from SKETCHUP is used for paper modeling means.
ADOBE ILLUSTRATOR Adobe Illustrator CS5 software helps you create distinctive vector artwork for any project. www.adobe.com/products/illustrator PROCESSING Processing is an open source programming language and environment for people who want to create images, animations, and interactions. www.processing.org GOOGLE SKETCHUP SketchUp is a 3D modeling program designed for architects, civil engineers, filmmakers, game developers, and related professions. It also includes features to facilitate the placement of models in Google Earth. It is designed to be easier to use than other 3D CAD programs. www.sketchup.google.com
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_polyPhonic _dhiyaMuhammad _2011 _singapore _delaunayTriangulation
DELAUNAY TRIANGULATION
In mathematics and computational geometry, a Delaunay triangulation for a set P of points in the plane is a triangulation DT(P) such that no point in P is inside the circumcircle of any triangle in DT(P). Delaunay triangulations maximize the minimum angle of all the angles of the triangles in the triangulation; they tend to avoid skinny triangles. The triangulation was invented by Boris Delaunay in 1934.
For a set of points on the same line there is no Delaunay triangulation (the notion of triangulation is degenerate for this case). For four points on the same circle (e.g., the vertices of a rectangle) the Delaunay triangulation is not unique: the two possible triangulations that split the quadrangle into two triangles satisfy the “Delaunay condition�, i.e., the requirement that the circumcircles of all triangles have empty interiors. By considering circumscribed spheres, the notion of Delaunay triangulation extends to three and higher dimensions. Generalizations are possible to metrics other than Euclidean. However in these cases a Delaunay triangulation is not guaranteed to exist or be unique.
PROPERTIES
Let n be the number of points and d the number of dimensions.
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The union of all simplices in the triangulation is the convex hull of the points. The Delaunay triangulation contains at most O(n⌈d / 2⌉) simplices.[3] In the plane (d = 2), if there are b vertices on the convex hull, then any triangulation of the points has at most 2n − 2 − b triangles, plus one exterior face (see Euler characteristic). In the plane, each vertex has on average six surrounding triangles. In the plane, the Delaunay triangulation maximizes the minimum angle. Compared to any other triangulation of the points, the smallest angle in the Delaunay triangulation is at least as large as the smallest angle in any other. However, the Delaunay triangulation does not necessarily minimize the maximum angle. A circle circumscribing any Delaunay triangle does not contain any other input points in its interior. If a circle passing through two of the input points doesn’t contain any other of them in its interior, then the segment connecting the two points is an edge of a Delaunay triangulation of the given points. The Delaunay triangulation of a set of points in ddimensional spaces is the projection of the points of convex hull onto a (d + 1)-dimensional paraboloid. The closest neighbor b to any point p is on an edge bp in the Delaunay triangulation since the nearest neighbor graph is a subgraph of the Delaunay triangulation.
_polyPhonic _dhiyaMuhammad _2011 _singapore _frequencyAnalysis
FREQUENCY ANALYSIS
The frequency spectrum of a time-domain signal is a representation of that signal in the frequency domain. The frequency spectrum can be generated via a Fourier transform of the signal, and the resulting values are usually presented as amplitude and phase, both plotted versus frequency. Any signal that can be represented as an amplitude that varies with time has a corresponding frequency spectrum. This includes familiar concepts such as visible light (color), musical notes, radio/TV channels, and even the regular rotation of the earth. When these physical phenomena are represented in the form of a frequency spectrum, certain physical descriptions of their internal processes become much simpler. Often, the frequency spectrum clearly shows harmonics, visible as distinct spikes or lines, that provide insight into the mechanisms that generate the entire signal.
A source of sound can have many different frequencies mixed together. A musical tone’s timbre is characterized by its harmonic spectrum. Sound in our environment that we refer to as noise includes many different frequencies. When the frequency spectrum of a sound signal is flat, it is called white noise also known as harmonics.
_polyPhonic _dhiyaMuhammad _2011 _singapore _frequencyAnalysis
Spectrum analysis is the technical process of decomposing a complex signa sum of many individual frequency components. Any process that quantifies versus frequency can be called spectrum analysis.
Spectrum analysis can be performed on the entire signal. Alternatively, a spectrum analysis may be applied to these individual segments. Periodic f division. General mathematical techniques for analyzing non-periodic func The Fourier transform of a function produces a frequency spectrum which c ferent form. This means that the original function can be completely reco reconstruction, the spectrum analyzer must preserve both the amplitude an be represented as a 2-dimensional vector, as a complex number, or as magn signal processing is to consider the squared amplitude, or power; in this In practice, nearly all software and electronic devices that generate fre mathematical approximation to the full integral solution. Formally stated sampled signal.
Because of reversibility, the Fourier transform is called a representatio frequency domain representation. Linear operations that could be performe easily in the frequency domain. Frequency analysis also simplifies the un operations, both linear and non-linear. For instance, only non-linear ope The Fourier transform of a stochastic (random) waveform (noise) is also r picture of the underlying frequency content (frequency distribution). Typ transforms are performed on each one. Then the magnitude or (usually) squ age transform. This is a very common operation performed on digitally sam of processing is called Welch’s method. When the result is flat, it is co often reveal spectral content even among data which appears noisy in the
al into simpler parts. Many physical processes are best described as a the various amounts (e.g. amplitudes, powers, intensities, or phases),
a signal can be broken into short segments (sometimes called frames), and functions (such as sin(t)) are particularly well-suited for this subctions fall into the category of Fourier analysis. contains all of the information about the original signal, but in a difonstructed (synthesized) by an inverse Fourier transform. For perfect nd phase of each frequency component. These two pieces of information can nitude (amplitude) and phase in polar coordinates. A common technique in s case the resulting plot is referred to as a power spectrum. equency spectra apply a fast Fourier transform (FFT), which is a specific d, the FFT is a method for computing the discrete Fourier transform of a
on of the function, in terms of frequency instead of time; thus, it is a ed in the time domain have counterparts that can often be performed more nderstanding and interpretation of the effects of various time-domain erations can create new frequencies in the frequency spectrum. random. Some kind of averaging is required in order to create a clear pically, the data is divided into time-segments of a chosen duration, and uared-magnitude components of the transforms are summed into an avermpled time-domain data, using the discrete Fourier transform. This type ommonly referred to as white noise. However, such processing techniques time domain.
_polyPhonic _dhiyaMuhammad _2011 _singapore _frequencyAnalysis
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polyPhonic
polyPhonic [mode&musings] is published by: DHIYA MUHAMMAD dhiyamd@gmail.com www.dhiyamuhammad.tumblr.com
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