Liminal Kicks

Liminal Kicks is my latest album, released on the new label Superpang based in Rome, Italy, with cover art designed by Joe Gilmore. It’s a collection of tracks made with drum synths and saturation. I used Mathematica to generate MIDI files with up to one million notes, where the timing between notes was programmed to change exponentially, with the exponent set to the Golden ratio (1.618 or 0.618). ‘Liminal’ derives from the ancient Greek limen (λιμήν), meaning ‘threshold’ or ‘doorway’.

In physiology, psychology, or psychophysics, a limen or a liminal point is a sensory threshold of a physiological or psychological response. It is the boundary of perception. (Wikipedia)

Each of these tracks crosses this perceptual threshold. As the drums increase or decrease in tempo and cross the threshold, our perception of it changes between rhythm and pitch. At lower speeds, we hear separate events as increasing/decreasing rhythm, but beyond the threshold we perceive a note or frequency, increasing/decreasing in pitch.

Rather than playing around that threshold, these tracks go well beyond it, exploring what happens when the tempo is very high. As the sounds speed up or slow down, there is often a cyclic or fractal pattern in the frequency shaping of the sounds. This is probably due to a kind of Moiré or wagon wheel effect, caused by the constantly changing difference between the length of the drum sounds and the tempo. You can see an example of the fractal kind of pattern in the spectrogram of track 4, ‘kick2tom1048576′:


The sound is made more complex by a saturation effect, which adds some non-linearity to the equation. Sometimes it also produces an effect like a Shepard or Risset tone, an audio illusion of constantly rising/falling pitch, where it generates tones that are overlapping (in time) and parallel (in frequency). You can hear this most clearly in the ‘snare16384’, and see it in the yellow lines in the image below:


In ‘kick5’, a symmetrical track, you can hear the cyclic patterns in the upper frequencies. In the spectrogram they form similar shapes at different scales. Counter-intuitively, as the duration between notes deceases, the size of the patterns increases, and vice versa. This is probably due to the exponential rate of change, which starts fast and slows down, or vice versa.

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MoCap Fractals

Today I learned that some suits used for motion capture use a pattern that’s a variation of the Sierpinski triangle fractal. I happened to see a tweet from @LASTEXITshirts celebrating the birthday of actor Andy Serkis (56 today), and I recognised the pattern on the suit he’s wearing.

Searching for ‘mocap suit sierpinski’ leads via a post on Reddit, Why do MoCap suits use Sierpinski triangles?, to the patent for the design in 2013 by Lucasfilm. The patent describes it as a ‘scale independent tracking pattern’. Fractals are, by definition, scale-independent, i.e. they look the same at any scale. A fractal pattern is useful for motion capture because whether near or far there are always some visible points to track. It also means that some of the pattern is visible when photographed in motion, when the image is blurred.

The pattern used for the mocap suits is clearly not quite scale-independent, however. It has only 4 levels (there are only 4 different sizes of triangle), so the range of scales is limited, but presumably this is sufficient for the job, given the size of a human body and the scale of the image on screen. Also, the pattern isn’t the same at all levels – only the largest triangles have smaller triangles within them – but it seems that this isn’t a key feature of how it works (my guess is that this particular pattern was designed to be protected by trademark.). Although the patent describes how the pattern is “configured such that a first portion of the pattern is tracked at a first resolution and a second portion of the pattern is tracked at a second resolution”, this doesn’t rely on the differences in the levels of the pattern. It’s the nested structure of the fractal, its self-similarity, that means different levels of the pattern can be tracked for different levels of detail. Triangles are suitable because they have identifiable points that can be tracked by computer vision algorithms, and these trackable points make up the polygon meshes used in 3D graphics.

As the resolution of the actor and the pattern changes, some trackable portions of pattern may become untrackable by the capture device, and some untrackable portions of the pattern may become trackable. When this happens, vertices may be added or removed from the mesh.

Other shapes shapes work too, if arranged in a fractal pattern. The patent also includes another design for a scale independent pattern based on circles. Like the triangular pattern, the idea is that more shapes can be tracked with greater resolution. By chance, the low resolution of the image in the patent illustrates this feature quite well.


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20×20 – Processed Field Recordings

Processed Field Recordings is my new album out on 20×20, a project and label run by Neil Stringfellow. I met Neil in 2015 on a course about field recording tutored by Chris Watson and Jez riley French, and we’ve both participated in the Disquiet Junto. Under the name Audio Obscura, his work includes Gathering Silence, an album based on recordings in empty old churches in rural Norfolk, and an interpretation of George Orwell’s Nineteen Eighty Four. 20×20 is a project involving 20 artists each making an album of 20 tracks that are 20 seconds long. The first album was released on 20th January 2020, with subsequent releases scheduled every 20 days. This is the 5th in the series, following albums by haLF unusuaL, Daniel Diaz, Tay_ploops and Minimal_Drone*GRL. 

The album and track titles are plainly descriptive. It’s based on recordings I’ve made over the past 10 years in different places: Nottingham city and riverside, a village in Rutland, a smallholding in rural Norfolk, and national parks in Derbyshire and North Yorkshire. The sounds are processed with various effects and some have synthesized sounds added. In all cases, some parameters of the effect or synth are controlled by aspects of the source sound, e.g. volume, pitch and timing. To varying degrees, the processing highlights and obscures elements of the sounds. This is my second album based on field recording. The first was Hyperchaos Volume 1, a collaboration with 23 artists, where the tracks were limited to a maximum of 1 second each. 

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6-Colour Block Cellular Automata

Here are some results of recent tests with 6-colour cellular automata. These are 6-state relations of the simple 2-state block cellular automaton that I use a lot, rule 39 in Wolfram’s notation which translates the output states of the rule specification into a decimal number. This rule family uses a 2-cell block, offset by one cell each generation, so it tiles in a brickwork pattern. Rule 39 of the 2-state CA translates to: “if both cells are in the same state, change state on the next step; if cells are in different states, remain in the same state.” In the program run in Mathematica, this is applied as a set of transformation rules operating on lists of numbers, e.g. {0,0}→{1,1}. The rule 39 specification is visualised below. The top half of each part represents the current states of the two cells in a block; the bottom half represents the states of those cells in the next generation.

The rule specification has one part for each possible configuration of states in a block. The number of different possible configurations with a block size n and with k colours is equal to k^n, so a k=2, n=2 CA such as rule 39 has 2^2 = 4 parts. A 6-colour CA rule has 6^2 = 36 parts. With 2 colours, there are k^(n×(k^n)) = 256 possible rules. With 6 colours, there are 106387358923716524807713475752456393740167855629859291136 rules. This is too many to explore exhaustively, so I explored a subset by using constraints. The constraints were to incorporate the k=2 rule 39 pattern within the k=6 rule with at least one pair of colours, and to generally conserve the colours by limiting the rule specifications to simple transformations. Rule 39 conserves the total number of different colours and is reversible, which means that any evolved state can be run backwards, by applying the rules in reverse, and it will return to its initial conditions. In general, a block CA is reversible if its rule permutes all possible blocks. Due to the constraints, many of these k=6 rules meet this criterion and are therefore reversible. This is rule 39 with random initial conditions, 32 cells in size, run for 32 generations:

For the purpose of these tests, the 6 states are represented by the colours red, orange, yellow, green, blue and purple (R,O,Y,G,B,P). The final colours might be different, depending on how printing tests turn out, but will have a similar spread, being equally-spaced on the trichromatic colour wheel. My aim is to find combinations of rules and colours that induce perception of secondary colours through optical mixing.

Below are some results of 6 different rules tested with three sets of initial conditions:

  1. Random choice of all colours (R/O/Y/G/B/P)
  2. Three pairs of complementary colours picked randomly (R/G, O/B, Y/P)
  3. Three pairs of neighbouring colours picked randomly (R/O, Y/G, B/P)

I generated one of each type of initial condition, and used this set of 3 for all rules shown here, to make the differences more visible. The initial condition is the top row, and time runs downwards from the top. Each CA is 240 cells in size, run for 240 generations.

Rule 20774264240371331443939583648662020862331869895110587700

This k=6 rule incorporates 2 instances of the k=2 rule 39 pattern: instead of black and white (B/W), it’s R/O and B/P. You can see these in the top-left and bottom-right corners of the rule specification above. Random initial conditions (top) produces a bit of a jumble, but a pleasing combination of colours. Complementary pairs of colours (middle) makes a nice gradient out of reticulated patterns. Neighbouring colours (bottom) makes a horrible geometric mess.

Rule 20796116639275282756883592732765805970567318290529775700

This rule incorporates 3 of the k2 rule 39 pattern with 3 pairs of neighbouring colours: R/O, Y/G and B/P. The random conditions produce a similar thread-like patterns. The patches of colour in the other two blend and interact nicely.

Rule 103541839025819083223383708563702745805808655480680346680

This rule also incorporates 3 of the k2 rule 39 pattern, with different pairs of colours: R/P, O/B, Y/G. Randomness makes mostly vertical structures jostling with each other. In the structured initial conditions, the rule makes for clear areas with rule 39 behaviour that are gradually encroached by tendrils from the neighbouring areas.

Rule 62556620991588118164093337622114268271511526829240714310

This rule incorporates 3 of the k2 rule 39 pattern, with pairs of complementary colours: R/G, O/B, Y/P. In this rule specification, if the colours aren’t one of these pairs then instead of behaving like rule 39, each cell takes on the state of its neighbour, so the colours swap places. The images below are different to the previous ones because the random initial condition produces the most orderly image, comprising vertical repeating patterns. This example is the best of the vertical type patterns yet. In contrast, the other two images are more chaotic than those in the rules shown above.

Rule 62312062993845184628174649112617059049519000489975636340

This rule is quite similar to the previous one but with only 2 rule 39 pairs(R/G, Y/B). I like the squiggly patterns produced by random initial conditions.

Rule 62556620991588118164089965300297552193934650966268789830

Although this rule is very similar to the previous one, it is unlike all the others because it doesn’t have the property of permuting all possible configurations. Two configurations instead of the usual one lead to a configuration of two yellow cells (YY): BB and PP. With all the other rules so far, if you count the total number of cells of each colour in the output parts of the rule specification (the lower half of each part), they are equal, with 12 cells of each colour. In this rule, there are 14 yellow and 10 orange, plus 12 of every other colour. For this reason, it is probably not reversible. It makes some lovely patterns.

Rule 20774264240899287841879128096849235332307474604780057880

This rule differs from all the others because it doesn’t incorporate the rule 39 pattern. In this rule, colours rotate. Whenever a block has 2 cells of the same colour, the colour changes to the next along in the spectral sequence (rainbow order). This property seems to make a mess of any structure in the initial conditions. Interesting to try the rule, but uninteresting results.

Rule 62556585801747746489648698620876988974236430593535788615

What’s different about this – easily visible in the rule spec – is that some same-colour blocks stay as they are (O,Y,B,P). Only R and G are like rule 39. Like most of the other rules, it conserves the colours and is reversible. Not the most interesting results, because this rule doesn’t mix things up as much as the others.

Given that most of these rules conserve the total number of different colours in the initial conditions, it’s surprising how much they differ in terms of overall hue. With the right set of colours, these images can produce the effect of seeing colours that aren’t there, through a combination of optical mixing and colour juxtaposition. This visual effect was explored by Sydney Harry, whose paintings I’ve seen at the colour museum in Bradford. I’d like to see if the effect can work with these CA patterns in print instead of on screen, so I’ll be doing some print tests soon. It would be interesting to see what they look like in large scale – wall-sized, where they can be viewed from near and far.

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Frequency Domain Filtering

On r/puredata Acreil posted this video of generative music made with a pure data patch. It shows a scrolling stereo spectrogram of the music, with wavy lines that represent slices of filtered noise where the filter is always changing. Frequency domain filtering works by applying the Fourier transform to the input signal, resulting in a frequency spectrum which you cut or boost in places, then transform again back into the processed signal. Acreil did a post about this with another video that explains how it works and a link to download the pure data patch. He explains that “frequency domain filtering is nice because what would be a convolution in the time domain becomes a multiplication in the frequency domain.” This kind of processing can make the kind of shapes in a spectrogram that you don’t normally see, because it can make the kind of sounds that you don’t normally hear. The only other thing I know of that looks and sounds similar is the end of the track ‘∆Mᵢ⁻¹=−α ∑ Dᵢ[η][ ∑ Fjᵢ[η−1]+Fextᵢ [η⁻¹]]’ (also known as ‘equation’) from the Windowlicker EP by Aphex Twin. This is the track with the face encoded at the end. The bit that looks like frequency domain filtering is the patch of squiggly lines on the left.

Here’s a close-up on that bit. The face has a different texture – lower resolution, made of lines, whereas these squiggles are smoother, which suggests different techniques were used to make them. But both processes, frequency domain filtering and synthesis from images, involve the Fourier transform. So does the spectrogram. This video of a visual introduction to the Fourier transform by 3Blue1Brown helped me understand what it does.

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Last year I probably spent more on music than ever before. I was trying to support emerging artists and new labels, especially those I’ve been involved with, but was also driven by the usual consumerist desire to acquire stuff and fear of missing out. I bought music on CD, vinyl, tape and digital. This year it’s been the opposite, mainly because I just can’t afford it now, but also in an effort to live more sustainably. So I’ve bought only a handful albums – downloads only, and kept my two Bandcamp subscriptions. I’ve relied more on newsletters, mailing lists and radio. And instead of buying new books, I’ve picked up some of the unread ones I already own. So this year’s list of stuff I liked is a bit more idiosyncratic than usual. I’ve also gone back to a mostly vegetarian diet and make all my own meals from scratch. It’s cheaper and healthier, and although it takes time there’s a therapeutic benefit to cooking, especially if you’re feeding others too, so it’s time well spent. One favourite new recipe is chickpea soup, which sounds dull and measly, but it’s bright yellow with loads of flavour and is surprisingly satisfying:

  • dried chickpeas soaked overnight, cooked for 3/4 hour with bay leaf and garlic clove;
  • chopped onion fried in spices – turmeric, fresh ginger, garlic, black mustard seeds, cumin, coriander seeds, black pepper, fenugreek, cloves, cinnamon;
  • combine chickpeas and onion in vegetable stock with chopped fresh coriander and a bit of lemon juice or hot pepper sauce.

A graph showing monthly average carbon dioxide in the atmosphere from 1958 to 2019, increasing from 315 parts per million to 410.

Monthly average atmospheric CO2 (ppm). Data source: NOAA, Mauna Loa

The chart above shows atmospheric CO2 measurements from the Mauna Loa Observatory, Hawaii. CO2 is just one measure associated with global warming which, in turn, is not the only driver of the current environmental crisis. But it is representative in the sense that it clearly shows what we have achieved in terms of tackling the broader problem: nothing. CO2 levels are at their highest and the rate of growth for 2019 is 4th fastest, so there’s no signs of a slowing increase, let alone a decrease. The reasons why we’re in this predicament are summed up well in an article by Neil J. Hagens, ‘Economics for the future – Beyond the superorganism’ [open access]. Hagens describes how, through our collective behaviour which is built on cognitive biases, human society is effectively operating as a “single, mindless, energy-hungry ‘superorganism'” that’s eating itself out of house and home. In theory, we are smarter than this big stupid creature, but in practice we’re not, yet (as the CO2 chart shows). Hagens discusses energy and the economy, arguing that “money is a claim on energy” because every good or service that money pays for ultimately uses energy. Similarly, “debt is a claim on future energy” because it drags into the present the consumption of resources that would otherwise take place in the future. Debt is therefore not only borrowed money but “borrowed energy.” There is a lot of debt right now, because for the past 50 years debt has been expanded to prop up economic growth and to allow us to consume beyond our means. This debt may not be repaid, as has happened time and again in history, as shown by David Graeber (Debt: The First 5,000 Years). Hagens concludes: “It is likely that, in the not-too-distant future, the size, complexity, and (literal) ‘burn rate’ of our civilization will be much reduced by forces other than human volition.” On a slightly more positive note, he ends by outlining a need for a new ecological economics:

Whatever we’ll call it, we are desperately in need of a set of guideposts and principles that include not only ecology but also biology, psychology, physics and emergent behaviors. This discipline will focus at least as much on ‘what we’ll have to do’ as on ‘what we should do’.

Various Artists – CODE234 (CO-DEPENDENT)
The CO-DEPENDENT label was on my list for 2018. I didn’t pick out any specific albums then, but this time I will. It’s worth repeating the fact that neither label nor artists make any money from this – it all goes to a charity. CODE234 has only 5 tracks but it’s 3 hours long. It starts with what sounds like hand-controlled filter sweeps of sustained tones by the mysterious dj??water??, whose music and remixes are always interesting. PARSA’s track is digital crunch with vowel-like movements. Victor Moragues packs loads of variations into his track that starts with relatively simple repeats and then gets more chaotic. GOHV make a disorienting sound with beating frequencies and pulsing tones. The compilation ends with Luke Corbin’s 2-hour drone that veeery slooowly oscillates in pitch.

This year Autechre gave us free stuff, including a hiphop mix on RA Podcast 687 and a 12-hour eclectic mix on Mixlr. The image below is a snippet from the Mixlr live chat – those comments made me listen to Chiastic Slide again. Best of all though is WARP TAPES 89–93. It’s ace to hear these old tracks, with elements that were later used in other albums. It’s mixed really nicely too, not just fading in/out, it sounds like the tracks are re-worked, with parts isolated, extended and interleaved with each other.

Screenshot of online chat text between Autechre and fans.BandCloud
A weekly selection of electronic music on Bandcamp and SoundCloud by Aidan Hanratty, mostly abstract/ambient and club music and things in-between. Usually delivered midday Friday, providing a nice wind-down to the week and a lead-in to the weekend. Aidan also commissions guest mixes, does a show on Dublin Digital Radio, and has released a compilation album, Missives. Sign up for the email here:

Bass Clef – Open Hand Real Flames (NTS Radio)
A series of radio shows by Bass Clef. Two of these I particularly like because they focus on instruments I used to play: church organ and steel pan. Other favourites are the Nina Simone special and the one on mechanical musical instruments. I also played the hell out of Bass Clef’s ‘Holy Days Wholly Dazed’, a long piece of evolving looped and filtered beats that starts with a simple chord stab that eventually becomes a layered arpeggio, while a single rumbling bass develops into a fluttering cascade of delays.

Konstantin Batygin et al. – ‘The Planet Nine Hypothesis’ (Physics Reports, 809)
The motions of trans-Neptunian objects in the Kuiper Belt suggest the presence of a larger solar body. This paper reviews the observational motivation, dynamical constraints, and prospects for detection of the proposed object known as Planet Nine.

Tina Besley & Michael A. Peters – ‘Life and death in the Anthropocene: Educating for survival amid climate and ecosystem changes and potential civilisation collapse’ (Educational Philosophy and Theory)
An article that argues for the inclusion of survival skills in education, given the threats posed by the climate crisis. Besley & Peters ask: “Do we have a moral, ethical, personal or professional obligation to now begin such conversations in educational and political arenas? Or should we not bother and just do nothing in light of life and death in the anthropocene?” They cite an article by Luke Kemp (2019) that looks at the history of civilizational collapse and identifies some indicators, including environmental sustainability, societal complexity and inequality.

Boogie Down Productions – By All Means Necessary (Jive)
Some old-but-new-to-me music. BDP’s second album from 1988. Honest, straightforward approach to lyrics (KRS-One) and beats (DJ Scott La Rock) but highly creative and very effective.

David Burraston
I had the pleasure of meeting Dave this summer while he was over in the UK. We went on a pilgrimage to visit the grave of Ada Lovelace, the pioneering programmer, at St Mary’s church in Hucknall whose crypt is also the resting place of her father, Lord Byron. She foresaw the potential for computers to “compose elaborate and scientific pieces of music of any degree of complexity or extent.” We also went to Green’s windmill in Sneinton, former home of George Green, a self-taught mathematician who made contributions to calculus and theories of electricity and magnetism. Dave and I have some things in common: living in Nottingham, working in scientific/technical jobs, doing a PhD combining art and science (that’s how I first came across his work, about 10 years ago), using cellular automata for creative purposes, and now making generative music and field recording. The difference is that he does it professionally. He’s also hugely talented and vastly knowledgeable. It was good to discuss music, synthesis, computing and complexity theory, and to talk about being a musician and researcher and surviving through your practice. Listen to Dave talk about all this stuff and his work in this interview for Radio Web MACBA:

Camping in Derbyshire with friends
We’ve been camping at the same spot in an ancient woodland for more than 20 years now. Usually twice a year, around Easter and autumn, but just once this year. It’s halfway up a steep slope, so you’re knackered after carrying the gear up there, but then you can sit, have a beer, and absorb the scenery and the sounds. Facing downhill you get a view through the canopy of trees, mostly beech and some oak. The site’s acoustic quality is defined by the valley that it’s part of, and by the woodland’s hillside that stretches up to the north and down to the reservoir in the south. Motorbikes can be heard from miles off. Honking geese on the water echo off the opposite hillside. Owls and woodpeckers reverberate through the trees. At night, when the traffic has died down, the woodland soundscape is pretty noisy with nocturnal insects, birds and mammals, which can keep you awake if you’re not used to it. Even when it’s dead still, the trees drop their leaves, twigs and branches. When it’s very windy, the roaring sound is all around but there’s always a louder bit that’s localised and moves around, as if God is using a leaf blower. The fireplace we made with the stones lying around has really bedded in to the landscape over the years and moss grows on it. Its shape has been slightly modified to include a space for cooking. By burning smaller sticks first, you can build up a bed of embers that can be scraped over to that part of the fireplace to grill food on one side, leaving the main fire burning for heat and light in the middle. To sit round a fire with friends, sharing food and stories, is one of life’s greatest pleasures.

DEAFKIDS – Metaprogramação
Raw, trippy hardcore. Brazilian rhythms, distorted guitars and delay effects.

I also like the other two techno albums by Mrs Dink, CODE091 and CODE187, but CODE777 has the most individual sound.

Kevin Drumm – Bandcamp subscription

William Fields – FieldsOS
Originally broadcast as a series of shows on in the first half of the year, with hour-long pieces of algorithmic music generated on the fly. William Fields describes how it was made: “FieldsOS was created with Javascript, using WebMIDI to talk to REAPER. Javascript handles the algorithmic/generative aspect. REAPER is the audio/sequencer engine.” The generative system makes different styles of music by setting or controlling different parameters. Some pieces are based on named genres, others on various arbitrary settings. For each show, the system was started and left to run without interference, unlike a live situation where the system would be steered. William explained to me that there are 240 parameters, each with 128 possible values, so the generative space of FieldsOS is 128^240, which approximately equals 10^500. This is a very big number. In comparison, the estimated number of atoms in the entire universe is 10^80. “But most of it sounds like crap,” he said, “so my job is to find interesting zones within that space.” This is a central problem in generative art, particularly in the digital realm where it’s easy to use large numbers. The possibility space of a generative system that’s complex enough to be interesting or useful is likely to be to too vast to map exhaustively. Any system complex enough to generate something interesting will in most cases produce something that’s either simple or chaotic. The interesting states are also the least likely. We may know that “the interesting stuff is in the middle” on the border between order and chaos (so said Gary William Flake), but with such a large parameter space it’s not easy to find. This is also what Dave Burraston’s PhD was about, in relation to cellular automata. FieldsOS represents a new set of solutions to that problem, a journey through previously unexplored algorithmic terrains of familiar and unfamiliar styles.

There’s a great variety of sonic textures in these three long-ish tracks. Synthetic sounds are produced, sequenced and mangled in different ways. Squelches, honks, bleeps and whines. But not harsh or aggressive; it’s more like a methodically paced scientific demonstration. It makes for some really nice spectrograms. The spectrograms may show the structure of the sounds (in terms of their frequency content and amplitude), but they don’t reveal how they were made – in fact, it makes this album even more mind-boggling.

M.R. James – Casting the Runes and Other Ghost Stories (Oxford)
One from the unread pile. Really enjoyed these stories, which I’d been meaning to read since reading k-punk on M.R. James. This year Hyperdub released Mark Fisher and Justin Barton’s audio essay, On Vanishing Land, which is based on and in the eerie landscapes that James wrote about.

Om Kalsoum – Enta Omri (انت عمري)
Another old-but-new-to-me album, Enta Omri is a well-known and well-loved piece of music in Egypt since 1964 that’s been re-released this year by Souma Records.

Scott A. Kulp & Benjamin H. Strauss – ‘New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding’ (Nature Communications, 10)
New research that show it’s worse than we thought. Based on the findings, they made an interactive map that shows the land expected to be at risk of flooding in the near future.

Tom Mudd – Brass Cultures (fancyyyyy)
Algorithmic music that makes use of software developed by researchers at Edinburgh University that models brass instruments. Tom Mudd makes unheard-of sounds by inputting physically improbable parameters into this model, using algorithms to control instrument size and shape as well as how it’s played, “such as breath pressure, lip frequency, lip mass, valve fingering, and even temperature“.

Nihiloxica – Biiri (Nyege Nyege Tapes)
Nihiloxica supported Aphex Twin this year. Hypnotic polyrhythms from a group of drums and atmospheric synth sounds.

Peter Oborne highlighted the political role of the media in an article on Open Democracy and runs a site dedicated to documenting the lies of Boris Johnson and his government

Angel Olsen – ‘What It Is’ (Jagjaguwar)
The rest of the album All Mirrors isn’t really my thing, but I like this track. 70s-style production with slapback echo and some nice strings on top. The bass and guitar sound like T. Rex and the drums play a lolloping rhythm a bit like Mick Fleetwood would.

Jim O’Rourke & CM von Hauswolff – In, Demons, In! (iDEAL)
Smeared chants and drones that ooze around and sound like they turn inside out inside your head.

George Orwell – Nineteen Eighty-Four and Why I Write.
Seemed like the right time to read these.

An album released at the end of last year, made with cellular automata. You can read my interview with PARSA about the album uvuuvu in an earlier post.

Paul Prudence – Ficciones
Paul Prudence is author of the Dataisnature blog and audio-visual artist, but this is his first album. I like the detail and the kind of nervous twitchiness to the synthetic and and recorded sounds in the first 2/3 of the album. Then there’s a softer collection of drones starting with the hazy track ‘Gravity Map’, before returning to the scratch and hiss of recorded sounds in the final track.

Marcin Pietruszewski – The New Pulsar Generator Manual (Remote Viewing)
It isn’t the actual user manual for the NuPG software developed by Pietruszewski. It’s an essay about algorithmic composition, digitality, experimental sound and sound art, citing Curtis Roads, Manuel DeLanda, and Fernando Zalamea.

Francisco Sánchez-Bayoa & Kris Wyckhuys – ‘Worldwide decline of the entomofauna: A review of its drivers’, Biological Conservation, 232
The first global review of published evidence on insect declines, this study shows the main causes to be (in order of importance):

  1. habitat loss, caused by intensive farming & urbanisation
  2. pollution
  3. biological factors
  4. climate change

SDEM – deloc (.meds)
I like all of Tom Knapp’s music. He had 3 or 4 albums out this year, including Index Hole which is a collection of barrelling beats and filter-sliced synths, but deloc is probably the one I’ve played the most. It’s an interesting mix of order and disorder, natural and synthetic sound. I also recommend Tom’s recent mix for Leftovers, LFTMX_28_SDEM, which he describes as “1 TAKE… (POST ELECTION RESULTS HATE MODE) FRIDAY 13TH DECEMBER 2019”:

sold – RA.689 (Resident Advisor)
Really nice mix of non-dance music by sold (Glenna Fitch, @glorbis).

James Tenney – Harmonium (New World Records)
I’m a fan of Tenney’s writing on music theory, particularly Meta + Hodos and META Meta + Hodos. This is an album of his work performed by the Scordatura Ensemble, including Harmonium and four other pieces.

Whitehouse et al. – Complex societies precede moralizing gods throughout world history (Nature, 568)
A new study about social complexity and how it developed in relation to religion, based on analysis of data representing 414 cultures over 10,000 years.

Moralizing gods are not a prerequisite for the evolution of social complexity, but they may help to sustain and expand complex multi-ethnic empires after they have become established. By contrast, rituals that facilitate the standardization of religious traditions across large populations generally precede the appearance of moralizing gods. This suggests that ritual practices were more important than the particular content of religious belief to the initial rise of social complexity.

One of the loudest albums ever. It has a dynamic range (DR) rating of 0, the lowest possible score, with an average difference between peak and RMS loudness of 0.3dB. Only four other tracks in my collection have the same DR – one is a mashup of 95 Metallica songs, and the other 3 are all by Kevin Drumm: ‘Another Odyssey Of Waiting’, ‘Blocking’ (2018, Bandcamp), and ‘Purge’ (2013, iDEAL). In contrast with the quite hectic sound of CODE139, XI-N also made some ambient generative music, Pulsar (Amek Collective).

xin – MELTS INTO LOVE (Subtext Recordings)
xin ≠ XI-N, this is a different artist. Well-produced individual tracks and a nicely structured album. Like Saplings Records, proceeds from sales go to planting trees. xin also did a good mix for FACT:

Fernando Zalamea – Synthetic Philosophy of Contemporary Mathematics (2012, Urbanomic / Sequence Press)
François Laruelle – The Concept of Non-Photography
(2015, Urbanomic / Sequence Press)
More from the unread pile. I bought these together a couple of years ago. They have similar format and design with details of artworks on the cover. Laruelle’s book has Moiré 3  by Liz Deschenes and Zalamea’s has Los regalos perfectos by Maria Clara Cortéz. I’ve got some familiarity with photography through formal training and experience, but I know little about advanced maths, so I expected Zalamea’s book to be the more challenging, but it was mostly the other way round. I struggled to read Laruelle’s writing and follow his lines of thought, so it wasn’t a very pleasant or enlightening experience. Zalamea’s writing was easy to read even though I had to look up some things to know what it was even talking about, but it was clear where the limits of my own knowledge were preventing deeper understanding of the text. It was much more highly structured, too, with lots of internal references and some simple diagrams and tables that were necessary and useful. Perhaps that says more about me than it does about the quality of the books. Both worth a read.

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This is an album released last year, but which I didn’t write about on this blog. So here’s a belated bit about it.

Like much of my recent work, A FIDS MONITOR is based on sonification of data. This is economic and geophysical data that measure the state of the world: time-series datasets of global temperature, greenhouse gases, polar ice extent, bitcoin and the UK economy. Using Mathematica I transformed the data into MIDI files, applying various mappings between the numeric values and MIDI note pitch, volume and timing. These were often basic mappings, where greater values map to louder volume and/or higher pitch.

In generative music, the mapping or translation between data and sound is arbitrary in the sense that there’s not one particular setup that is ‘correct’ or ‘truthful’. But the choice of mapping determines which features of the data are audible and how they are heard, e.g. its structure, its range of values and its rate of change. This makes it an aesthetic relationship as well as generative (computational/procedural/productive/creative). These particular datasets don’t have a naturally musical structure, so it’s a challenge to make something musical with it, but I like to work this way, using constraints as a creative spur. It’s an approach I’ve grown into, mostly by not being able to afford much gear, partly through evolving taste/style, and it’s been developed by participating in the Disquiet Junto which sets a weekly task to make music under specific guidelines. In this case, the rules were:

  • use the whole of each dataset at least once
  • apply simple mappings to the data, and make simple MIDI files
  • use few instruments
  • make each track sound different
  • make sounds only from the data; no external sound sources

I arranged the MIDI files in Reaper. Most of the sounds are made with Razor – the VSTi synth designed by Errorsmith and Native Instruments, XILS3 – a VCS3 emulator, and Sforzando sample player with the Fluid R3 General MIDI sound font. Using Reaper’s parameter routing capability, I set up connections between the MIDI data and the synths so they interacted and created more variety within the constraints of the simple MIDI input. In this way, as processes become non-linear, the sounds become more complex.

Making the tracks just seemed to flow for once, and I didn’t tinker endlessly with each one. I’m quite pleased with how it turned out. Below are some details about the music and the data, with some charts to show the structure of the datasets. I hope it adds to the listening experience.

Global Surface Temperature

Met Office Hadley Centre. HadCRUT4 (anomaly w.r.t. 1961–1990 mean). 1850–2017. Monthly. °C.

NASA. GISS Surface Temperature Analysis (GISTEMP – deviation from 1951–1980 mean): Combined Land-Surface Air and Sea-Surface Water Temperature Anomalies (Land-Ocean Temperature Index, LOTI). 1880–2017. Monthly. °C.

These two datasets represent global warming. Track 1 uses monthly readings and starts from 1880, when NASA started measuring, so both datasets are present throughout. The GISTEMP dataset is played with sample-based percussion and HAadCRUT4 with a synthesized pitched sound. The increase in temperature over time is only really audible in the synth that rises in pitch. Track 2 is similar but uses annual averages instead of monthly, so there’s fewer data points / musical events. Track 3 uses the whole of both datasets, so you first hear the HadCRUT dataset before GISTEMP joins in. Track 4 uses only the Met Office data.

UK Deficit

ONS. Public sector current budget deficit, excluding public sector banks. 1946–2016. Quarterly. £ million.

The deficit is the difference between money raised and spent by the government. Track 5 is made with two copies of a MIDI file played in sync. One plays a synth bass and the other General MIDI percussion. The sonification works well here, highlighting the annual cycle in the quarterly data from the 1940s to the 1960s, where one quarter with a small surplus at the start of each new budget is followed by three quarters with a small deficit. Then it becomes more unstable and the government accounts go further into the red.

UK Balance of Payments

ONS. BoP Current Account Balance SA. 1955–2016. Quarterly. £ million.

ONS. BoP: Capital Account Balance CP SA. £m. 1955–2016. Quarterly. £ million.

The balance of payments measures the inward and outward transactions of the residents of a country (in this case, the UK) and the rest of the world. The current account is the national net income – how much money the country has. The capital account balance consists of capital transfers and trading of land and natural resources. Added together, they show whether a country is a lender (in surplus) or borrower (in deficit) to the rest of the world. Track 6 interleaves the datasets, with the capital account data (bass sound) half a beat behind the current account data (drum sound, a modified version of Errorsmith’s excellent ‘Kick to Tom’ preset in Razor).

UK Employment & Unemployment

ONS. Employment rate (aged 16 to 64, seasonally adjusted). 1971–2016. %.

ONS. Unemployment rate (aged 16 and over, seasonally adjusted). 1971–2016. %.

These measures of employment and unemployment rates are inversely correlated but do not quite sum to 100% because they represent different definitions of employment and different age groups. Track 7 plays both datasets in parallel using bass and drum sounds.


Blockchain. Market Capitalization (The total USD value of bitcoin supply in circulation). 15/04/2016–14/04/2017. Daily. USD.

Blockchain. Average Block Size (The average block size in MB). 15/04/2016–14/04/2017. Daily. MB.

Blockchain. Transaction Rate (The number of Bitcoin transactions added to the memepool per second). 15/04/2016–14/04/2017. Daily. Transactions added per second.

All three datasets are used in each of tracks 8, 9 and 10, but each uses different instruments and different parameter modulation setups.

Sea Ice

National Snow and Ice Data Center. Arctic sea ice extent and area. 1979-2016. Monthly.

National Snow and Ice Data Center. Antarctic sea ice extent and area. 1979–2016. Monthly.

This polar ice extent data shows the annual cycle of increase and decrease, and how the polar cycles are 180° out of phase with each other. It also shows how much the northern ice caps are melting. Tracks 11, 12 and 13 all use both datasets. Each track uses different sounds, but within them both MIDI files use the same sound. In each track the north can be heard on the left and south on the right. The image at the top of this post is based on the difference in northern ice extent from the monthly average.

Greenhouse Gases

NOAA. CO2 air samples collected in glass flasks, Ascension Islands station. 1979–2015. Monthly. ppm.

NOAA. CH4 air samples collected in glass flasks, Ascension Islands station. 1983–2015. Monthly. ppb.

These are measurements of the two main greenhouse gases, carbon dioxide (CO2) and methane (CH4). The datasets include the time of day at which the readings were taken. I used this in the music to set note duration. You can hear this in the irregular timings of the kick drum in track 14, which uses just a 3-year span of data. The data clearly includes anomalous readings – the visible outliers on the charts. I included these anomalies in the translation into MIDI files. You can hear them clearly in track 15, which only uses the CH4 data, and where the timing is also based on the same method as before. Track 16 uses both datasets in full, with the same anomalies and the same mapping/timing method. In this, the longest and final track, the rising CO2 levels are audible in the rising pitch of the synth sound.


I recommend Philip Sherburne’s recent article, ‘How Experimental Musicians Are Soundtracking the End of the World‘, featuring Matmos, Hildur Guðnadóttir, AGF and Tanya Tagaq.

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Yemen Data Project

I’ve got a new EP out today on the New York Haunted label. It’s based on sonification of data on air strikes in the Yemen war. The data is from Yemen Data Project, an organisation that collates, analyses and shares information about the long-running civil war between government and Houthi forces. In the absence of official records, the project team collects data on political violence and on the air raids conducted by the military coalition led by Saudi Arabia and the United Arab Emirates and backed by the US and the UK. I used the latter dataset, covering the period from 26/03/2015 to 15/12/2017. You can download the latest datasets here: Any money I make from this EP will be donated to the Yemen Data Project.

I used pivot tables in Excel to transform data on the variable Main Target Category in combination with date and time. The categories are: civilian, cultural/heritage, economic/infrastructure, educational facility, infrastructure, international community, media, medical facility, military security target, political/tribal and unknown. Using Mathematica, I re-scaled these lists, mapped the data to notes, and outputted as MIDI files. I arranged the MIDI in Reaper and played them through Razor synth. In each track, just over 2 years of data is represented in around 2 minutes of music. Different categories of target can be heard as different sounds. The simple mapping of data to music means that the louder it is, the more air strikes are represented.

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PARSA – uvuvu

The latest album by PARSA makes for some nice spectrograms. PARSA has previously made music with cellular automata (CODE147) and has recently collaborated with Ramtin Niazi (CLEANBOUNCE VARIATIONS) and with Beckton Alps2 as PARSALPS (Reductions). Here are some of spectrograms of uvuvu and a discussion we had about the music.

1 didone256hanning2

[Me:] First of all, how did you make this album? The track titles include references to sampling window functions, including the names and sizes of different types of window (‘hanning’, ‘blackman’; 64, 128, 256). Do the titles relate to the way that the tracks were made?
Window functions happen to be the basis of spectrograms. The window size determines whether the accuracy and resolution of the image is concentrated more towards time (with a short window) or frequency (with a long window). The different window types produce different kinds of analysis errors / visual artifacts. This is what makes the visible false frequency bands that run parallel to the actual frequency, like ripples or echoes. In the spectrograms of your uvuvu tracks, it makes some really nice patterns.

2 qqqq3_64hanning16

[PARSA:] The album is comprised of recordings I’d made while prototyping a sequencer in Unity. uvuvu was actually its working title. I would have it send MIDI off to Ableton Operator, or a Nord Drum 2 or a desktop Blofeld. The result of this endeavor was a sluggish sequencer, the realization that I was actually better off coding my ideas instead of dragging patch cables around with a mouse, and hours of recorded material which I abandoned for a year or two. I later on started using Cecilia5, and reached for this big pile of audio as the basic material. The source material is that, and all later processing was done in Cecilia, using the Vectral module. So each track title is: {source-file}{FFT-Size}{FFT-Envelope}{FFT-Overlaps}. Describing the settings of the module. Intentionally named so, so I could return and tweak things, because there were too many files. I did not know much about sampling window functions at the time, and I intentionally refrained from looking them up.

I do programming for a living and a big part of that to me is to know the ins and outs of what I’m working with. But when working on my own stuff, I need to NOT know something about what I’m working with, otherwise I’ll move on. Maybe that’s why everything I’ve released so far doesn’t sound polished, or complete or whatever. I record the process, not the product. So these settings were chosen just because I liked their effects on certain source material!

I recently dug up uvuvu in preparation for a talk I did at Tom Mudd’s class at the University of Edinburgh.

“4 chimes256hanning2 or my attempt at learning about Markov chains”

“8 mainline_rework. This was around the time that uvuvu worked offline and exported MIDI files”

Looking at these, I would do a lot of things differently if I wanted to start writing this thing now. This project failed pretty bad!

3 0015064hanning8

[Me:] Right, so the track titles relate directly to the parameters of a FFT vocoder. It’s definitely useful to use file names like that when experimenting, to keep track of things. It’s interesting that you’ve worked with Tom Mudd – I like his synthesis work. Do you often do that kind of talk, or get involved with teaching?

I think I know what you mean about not quite knowing what you’re working on. I’m also interested in the process as much, if not more than, the product. One idea that’s been useful for my own work is Philip Galanter’s definition of generative art ( Its defining element is that the artist gives away some amount of creative control over to a generative system. It is this element that often makes generative art unpredictable from the artist’s point of view when making it (usually only within certain carefully specified parameters) and that’s often part of the reason for using generative systems. The system could be a set of rules to follow, a physical mechanism, or code executed on a computer, but in all cases what makes it a generative system is that it does some of the creative work that would otherwise be done by the artist. In your work on this album, would it qualify as generative in terms of this particular definition?

Why do say it failed, and what would you do differently now?

4 chimes256hanning2

[PARSA:] I enjoy Tom’s music. He’s doing some crazy stuff with cool tech, can’t wait to hear more. We live in the same city and meet once in a while and talk about what we’re working on. He was kind enough to get me to talk about my approach in his class. I don’t have any real teaching experience, or formal education for that matter. Preparing for it however helped me realize a lot of things about my own work. Its title was “Zen an the Art of Making Mistakes”, actually (I know…). So I guess it’s a bridge to the next topic.

All the tracks are definitely generative. All produced by tiny systems with Markov chains + feedback as their prominent features, in this instance. I surely prefer listening to music over creating music, so generative systems that run with little interference from my end are perfect for me. Maybe what I’m trying to do is not so different from what a photographer or documentary maker does. I just show up and decide what to record. But I’m not sure about actual unpredictability in my work. Particularly when parameters can be/are (carefully) specified, in a digital environment. For instance: So, maybe it has to do with scope, or the magnitude of change? No idea! I’m not smart: I still have to look up simple sorting algorithms after 10 years of programming, or use a calculator for simple multiplications. But that’s why I’ve chosen to work this way. Because every mistake I make seems like a suggestion made not by “me”, and I could use a lot of that. If I were to rely on what I know, I’d do very little.

The software failed because it was providing a solution to a non-existent problem. I realized I didn’t need to create a generic tool at all. I was creating an environment in Unity that allowed me to build systems by putting together buildings blocks I’d have to write.
Which is to say, I was using the building blocks of a programming language (system) to create another system that would enable me to do so. So it was a sub-language defined on a sub-language that was built on top of another couple of languages. Much like how we go about making other things nowadays. :) That level of abstraction was completely unnecessary, so the current version is only a few hundred lines of C code that supports live-coding by hot-reloading libraries.

5 bev128blackman4_8

[Me:] Good stuff. Thanks for your responses – it’s been very interesting.

uvuvu is available via Bandcamp:

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Harmonic Series

Harmonic Series is a set of tracks I made in 2012 by generating pure sine waves at harmonic frequencies and arranging them in various sequential patterns. The sine waves were generated by writing some code in Mathematica, creating sets of tones with different harmonic ratios and amplitude envelopes, and the resulting files were sequenced in Reaper. At the time there was a web app that created polar coordinate spectrograms of music you uploaded to it (I forget what it’s called, and can’t find it now), with which I made some images of these tracks – these are on the album artwork. Below are some recent spectrograms of the same tracks made with Sonic Visualiser, using a setting that more accurately identifies frequencies, which are visualised as thin lines of colour instead of wide bands. Click images to enlarge – they look best at 100% or bigger.

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