Synthesizing Environments

While the musical cues for this project are implemented as sets of generative scripts, another component for the audio side of things I have been working on is based around the idea of a synthetic environment. Inspired by other hyperreal field recording projects like Luc Ferrari’s Presque Rien series, Michael Pisaro’s July Mountain or Francis Dhomont’s Signe Dionysos which (more or less) don’t immediately reveal themselves as synthetic environments even though they might be composed of impossible or at least unlikely components. (How did that train get inside the frog pond??)

In 2016 I started collecting field recordings in multiples to use in constructing new synthetic environments – each one based on a real environment. I only have two so far: ~30 hours of recordings on my porch made at the same time each day for a month, and a handful of recordings of muffin baking in my kitchen. I started cataloging interesting moments in the recordings in a notebook – at 23 seconds a short bird chirp, 32 seconds a distant metallic clang, etc. It was a great way to spend a few days; field recordings cranked up on the stereo just listening and writing, but after doing about 5 hours worth I realized I really needed to automate the process somehow.

Last year I finally picked up a machine learning book, hoping to be able to train an algorithm on the recordings and have it classify them based on low level features extracted from the audio. The classic example of this is a dataset of Iris petal/sepal lengths & widths used to predict the species. Given a fixed set of labels (one per species) a collection of measurements can be used to predict which species it best matches. This is basically what I was looking for, but would require a training dataset with human-provided labels to learn from. Rather than try to do a supervised process where I’d take my original notebooks and use them to come up with the labels for the classifier (this is a bird, this is a car engine, this is a distant metallic rattle…) it seemed more interesting (and probably less tedious) to take an unsupervised approach and try to have the algorithm infer classifications and groups from the data itself.

I decided to start by focusing on the spectral centroid of these recordings because of this really cool study by Beau Sievers et al on the correlation between emotional arousal and the spectral centroid. The spectral centroid is the mean frequency in a set of frequencies – a sound with lots of high frequency energy and low frequency energy could have a centroid somewhere in the middle of the spectrum, while a pure sinewave at 200hz would have a spectral centroid of 200hz.

An initial experiment doing analysis on fixed-length overlapping grains didn’t go very far. I segmented the field recordings into small overlapping grains, found the spectral centroid for each grain, and then reconstructed the sound by shuffling the grains around so they would go in order of highest spectral centroid to lowest. Instead of the smooth sweep from high-energy sounds to low-energy sounds that I imagined, the result was basically noise. I was bummed out and left things there.

The wonderful folks at Starling are working on a cool project that involves doing analysis on a set of field recordings of train whistles. On Monday I had a long conversation about their process so far and the analysis approaches they’d been trying. It got me excited to pick up on this project again and find a better approach to segmenting the field recordings for analysis – instead of just cutting them into fixed sized grains which seemed to produce mush.

This weekend I updated the script I was working with to do segmentation using the aubio library’s onset detection, breaking the field recordings up into segments between onsets instead of arbitrary fixed-length slices. The script does an analysis pass on each sound file (usually about an hour of audio or so per file) – finding segments, doing feature extraction (spectral centroid, flatness, contrast, bandwidth and rolloff) on each segment and storing the results in an sqlite3 database to use for later processing.

That’s pretty much as far as I got this weekend! Doing one pass of analysis on the entire dataset takes about 3 hours so the only tuning to the analysis stage I’ve done so far is to low pass the audio before doing analysis (at 80hz) which I hope compensates a bit for all the low wind noise rumbling in the porch recordings.

Below is a new test reconstruction, doing the same type of sorting on the spectral centroid – highest to lowest – but placing each segment on an equally spaced 10ms grid, cutting down any segments longer than 1 second, then applying a little amplitude envelope (3ms taper plus a hanning fadeout) and stopping after accumulating 5 minutes of output. (Which means this places roughly 30,000 variable-sized overlapping audio segments at 10ms intervals in order of highest spectral centroid to lowest in the space of 5 minutes.)

Segmenting the sounds based on onset detection is already producing way more interesting results! I’m looking forward to studying the data and tuning the approach – and, one day trying to wrap my head around the machine learning component of this to do unsupervised classification of the sounds into a 2d space, so instead of simply moving from highest to lowest across a single feature dimension (the centroid) I can play with moving through a parameter space that hopefully has a meaningful correlation to the content of each sound segment. I love the idea of being able to move slowly from the region of the birds into the region of the revving of the car engines and so on.

Taking this approach it would be possible to match environments to locations in a story, and move through the environment’s sound-space in some meaningful-sounding way that correlates to the generative action in the story. If Pippi is at home in Villa Villakula and is visited by an annoying fancy gentleman, the environment could shift positions in the parameter space along with the mood of the characters or the intensity of the action etc – and allowing for that to be controlled by an automated process would let the environment change with the story even though the story itself may be indeterminate.

Anyway, here’s the most recent test render from this afternoon – things begin in muffin-baking world and slide off into the sound-world of my porch pretty fast. The church bells really start to clang by the end!