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- Polywav easiest way to sync 9-channel playback in this case

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Why 9 Speakers?
There are 9 speakers set up  to replicate each of the three series (of 9 photos), showcasing the 30 second sonification result of each individual photo out of the overall 27.

- Other 9 channel multi-speaker arrays include Second Order Ambisonics, though I wanted specific channels dedicated to specific speakers for this work.

How?
Focusrite Scarlett 18i20 gave me 18 inputs to work with.

In line with the overarching concept of Hegel's Owl of Minerva, and there being clarity at dawn once confusion subsides, I decided to upscale the audio fidelity in each series.

Speaker Array:

Photo (1) -
Photo (2) - KRK V4
Photo (3) -  KRK V4
Photo (4) - Adam TV5
Photo (5) - Adam TV5
Photo (6) - Genelec 8030c
Photo (7) - Genelec 8030c
Photo (8) - KRK Rokit 8
Photo (9) - KRK Rokit 8

I had planned to work up from lower quality devices, such as bluetooth speaker; phone; tv; laptop; but couldn't get a suitable cable selection for this in time that would work with the Focusrite Scarlett 18i20 (lots of adapters needed). Also TSD very busy so had to use speaker pairs for 2 numbers. Could do in future 27 speaker exhibition ; )

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- Played film via USB on TV in front of the array of speakers
- The film was same from the 3 speaker test, showing each series for 4 mins 30 secs (each photo gets the 30 seconds of music i created from the relevant data)

- looks not as clean as projector but could fix this in a legitimate exhibition situation

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- Kept Zoom H3 stationary in front of screen for recording of first two series of photos

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- Got final series recording walking around with the microphone at  a rough head height to replicate hearing the 9 speaker array in exhibition

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G = Wolf [Genelec]
&
R = Dog [M Audio]

B = Time [SN4A

They played in sync with video using two media players. Bit tricky, had to download code to assign one of the players as main and other as follow.
Made polywav seem easier option for 9 speaker version.

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reduced photo dimensions too comfortable amount of data (original dimensions were 5504 × 8256 - TOO MUCH!)

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- Exploration into image and series/sequence, particularly referencing to the typological work of German photography duo Bernd & Hilla Becher

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values must be between 0-1

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The Owl of Minerva flies at night.
- We can only know with hindsight

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Decoded to binaural stereo mix with IEM plugins

- decodes from FOA (4 channels) > Binaural (2 channels)
- binaural playback not optimum for speakers due to phase cancellation as a result of time delays

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Uses input data to create midi notes from a pre assigned chromatic scale

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- Korean phrase

In the words of my collaborator, Joon:

"The meaning of the title of this work, 'Dog and Wolf Time', is that at dawn, everything is cloudy and only shadows are visible, so we cannot tell whether it is a wolf or a dog.

Like Minerva's owl, we can tell the difference only after everything has gone through a process of change. The goal is to see what similarities, differences, and relationships there will be between images in the same environment but different dates taken."

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- Set up R, G , B channels in Ableton and record for 30 seconds per photo.
- I now know how the data from R, G, B all sound for each photo of each series.

Could stitch together all 27x 30 seconds of midi, but would like more complimentary sound source + further possible sonification opportunities (automation of sound - timbre, depth)

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- r, g, b all assigned to digital piano
- sounds like whole tone music
- Low histogram values meant a general absence of higher notes. Lots of C1 and below notes. Altered this to achieve an audible result, thought this tampers with the process no less than /100 in excel.

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R = Dog Howl
G = Wolf Howl
B = Clock Tick

Samples: https://on.soundcloud.com/BRjou

Fitting with concept, and forgiving for lack of cohesion in notes. There was a possibility that I could further restrict the midi notes selected within the chromatic scale to a particular major or minor scale.

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Listen to Dog & Wolf Time sound sources by 91s12u31m1a11k #np on #SoundCloud

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- Patch scans from photo left to right as a person would view a photo. This is supposed to replicate human sight.
- The patch displays the r, g, b values whilst the scanning image. This provides 3 sets of data which can further influence the recorded midi.

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Image scanning (intended to replicate movement of someone's eyes looking at photo

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- Automating cutoff for 30 seconds based on changing floating point seen above. (this was then *500 to make the effect more audible

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Using mean value of selected pixels from each photo to automate filter cut off

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- Repeating process automating reverb pasted in from help Extras

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- After filter automation, I bounced and swapped the new recording into the sound file player
- I used sfRecord to do this. It didn't matter that the exports were mono, as the final stems were going to be assigned to individual speakers within a 9 speaker array.