After our dreams fade and a new day is gifted upon us, what do we see every morning when we first look outside? As curtains part and blinds are pulled, we glance out of a familiar window at a similar, yet subtly changing scene. Shifting drifts of cloud cross a distant sky blessed with a solar glow rising from the horizon. A row of awakening houses is serenaded by birdsong. The rumble of engines signals the start of daily commutes. A roll of undulating hills glisten with dew in the near distance. What did you see out of your window first thing? If that scene could sing what would its morning song sound like?
These are some of the abstract questions that will be explored in the latest project from electronic music producer Tomoroh Hidari. ‘12 Months of Morning’ will be a musical exploration of daily photographs of morning skies taken from Hidari’s current home in London and any other locations the year might bring. Beginning in February 2024 and concluding in January 2025, the origins of this diaristic project began as a daily ritual to take a photo out of a window as part of Hidari’s wake-up routine which included an espresso and cat cuddles. Hidari would post these scenes on social media with the greeting ‘おはよオーリバー (Ohayo-liba-)’ – a portmanteau of the Japanese ‘ohayou’ and their first ever name, Oliver: “Good Morning Oliver.” These images are “a friendly ‘hello world’ … a breeze of fresh air … a book of days in pixels.”
At the heart of Hidari’s 12 Months of Mornings project is a transformation of the abstract visual patterns and textures that can be captured in his photographs into their mirror world sonic counterparts. Sonification is a process of using non-speech audio to convey information or perceptualise data. Using an image-to-audio programme can convert the visual data of an image – in this case a photograph of a landscape, but it could be the rate of the clicking of a Geiger counter, the rise and fall of financial markets, or the daily tracking of air pollution of a city – and generate audio from this data. Whatever the data represents, the image of this gets played from left right, as if it is being read like a book. If this data is plotted on a graph, then it would track the converted time of the image versus the exported frequency of the image – “variations on a theme on a polychrome scale ranging from grey-ish grey … to more colours than a 90s SONY commercial.”
Hidari admits that 12 Months of Mornings will follow an arbitrary process “that much more reflects artistic choices in how data is converted to sound, then it does about the actual data.” The exported data from the photography will form a pool of sounds to treat with effects as deemed necessary by the artist. From changing the exported frequency and limiting the overall frequency range, to horizontally flipping the image to generate a different sound, or introducing delays, plugins and alpha and saturation controls, the sonic output of each monthly captured ‘morning sky’ will evolve as the year unravels.
Check out this short YouTube video (https://www.youtube.com/watch?v=7vj9BBYWnzw)
of Hidari demonstrating how sound art software was used to generate sounds for the first track of the release, ‘12th February 2024’. Across nearly 6 minutes, this atmospheric piece begins with rising waves of jittery particles which sound as if you are listening to a rotating helicopter blade beneath buried ice. Before giving way to nervous glitchy tones that point to an anxious attempt at breaking the surface to gasp for air. A distant siren-like drone appears halfway through, competing with coughs of motorboat engine splutters and rattles that eventually fades out to extra-terrestrial tones and textures. 12th February 2024 is very evocative of an early morning, bleak winter’s day. I picture a lonely jetty looking out over a frozen lake. Clouds are low and heavy. Spring is close, but not quite ready to melt the slow dread of winter tension.
As well as using different software to explore ways of sonifying photographs, Hidari also has plans to use tools to transform his images to MIDI. As an alternative approach to traditional sonification, this sonification by replacement “can be used to calculate ‘feelings’ and the result data will ‘sound like’ the feelings calculated.” (Kirke, Miranda 2014). As an example of an exploration into Pulsed Melodic Affective Processing (PMAP), the computational protocol is musical data itself, rather than sonifying a data stream.
With a photo being shared each morning, audience – or follower – participation will play a part in Hidari’s monthly process. The source photo for every month’s track will be selected by choosing the photograph with the most engagement across the artist’s Twitter and BlueSky accounts. This photo will then be run through Hidari’s range of software and transformed into the latest monthly sonic instalment.
I’m very curious to see how this project develops throughout the year. With the first track available on Bandcamp, the release will grow with each passing month and collect an expressionist calendar year of visual-to-audio compositions. Currently set to pay what you can, 12 Months of Mornings will gradually increase in price throughout the year, so now is a great time to introduce yourself to this project.
You can follow Hidari’s blog (https://www.ivorybunker.com/12-months-of-mornings/) for regular updates on the process and support 12 Months of Mornings on Bandcamp.
https://ivorybunkerrecordings.bandcamp.com/album/12-months-of-mornings
An Austrian living in the UK, Hidari releases on their own Ivory Bunker Recordings (https://ivorybunkerrecordings.bandcamp.com) and has previously released on Mahorka (https://mahorka.bandcamp.com/album/-), Isolate Records (https://ivorybunkerrecordings.bandcamp.com/album/the-necrophonicon) and Record Label Records (https://ivorybunkerrecordings.bandcamp.com/album/dubnihilism).
X/Twitter: https://twitter.com/TomorohHidari
Bluesky: https://bsky.app/profile/tomorohhidari.bsky.social
Instagram: https://www.instagram.com/tomoroh_hidari
Facebook: https://www.facebook.com/futuretomorohhidari
Ryan Hooper
MEANS 