Tuned In was a masters project collaboration between Alexander Einoder, Xinmin Jia, Xuansheng Wu and myself that was displayed as part of an exhibition at the McClelland Gallery Sculpture Park and Gallery in Frankston, Victoria in December 2020. The project involved using electronic sensors to detect small particle density levels in the atmosphere as well as traffic noise pollution. The resulting data could then be converted into values to be used to manipulate parameters in a custom software synth created in Supercollider to produce a live and dynamic sonification.
The resulting sonification was transmitted via FM radio to a receiver and loudspeaker built into an ‘alien observer’ helmet sculpture representing the narrative of the installation.
A python script incorporating the OSC framework was developed to send live data from the particle sensor out of Supercollider to a html file to be displayed on a webpage.
The project narrative was designed to highlight the impact of the built environment on biodiversity, and the effect of biodiversity loss on ecosystems and the future of the planet.
Gallery Description
The burnt-out helmet is a single piece of debris from the crash landing of an Earth- bound extra-terrestrial ‘observer.’ This observer is searching for a nearby probe that had arrived earlier and is located somewhere in the sculpture park. The probe transmits data measured from dust particles (particulate matter) and anthropogenic noise pollution in the atmosphere to the observer’s helmet in the form of sound (sonification).
The observer is concerned with particulate matter and noise pollution from vehicles using combustion engines because it signals that planetary inhabitants are unsustainably using up their planets finite resources and are at risk of greatly reducing biodiversity. This can lead to a global catastrophe by tipping the balance of fragile ecosystems and causing irreparable damage to the life support systems that enable the survival of all species.
At the crash site, the real time sonification of the data transmission can still be heard coming from the observer’s helmet.
Sensors in the probe measure the following:
PM2.5 and PM10 mass concentration per cubic meter. PM2.5 and PM10 are primary pollutant dust particles that are equal to or smaller than 10μm (micrometres) and 2.5μm in diameter. PM2.5 is produced from the combustion of materials such as fossil fuels, organic matter, rubber and plastic. PM10 is produced from sources including sea salt, pollen fragments and combustion activities such as motor vehicles and industrial processes.
Noise pollution from anthropogenic sources such as vehicles. The anthrophonic noise generated by the built environment can disrupt a natural habitats sonic footprint and the bioacoustic niches occupied by native land fauna and marine life within.
Sound Design
The changes in the sounds contained in the audio recording of Tuned In are all triggered by sensors detecting changing levels of noise and particulate matter in the surrounding environment. The following sounds can be heard within the recording:
Crackling noise: The number of crackles per second is equal to the average mass concentration of particulate matter measured in micrograms (μg) per cubic metre that is equal to or less than 10μm (micrometres) in diameter detected by the particulate matter sensor.
Pink noise: Pink noise is triggered by the intensity of sounds in the surrounding environment such as traffic, car horns and voices that are detected by a microphone. The louder a sound, the louder the pink noise. The pink noise has been programmed to provide a level of comfort to the listener similar to that of rainfall, wind or an ocean wave washing against the shore – all of which are examples of pink noise occurring in natural physical systems.
Pulsating tone: The pitch of the pulsating tone represents the Australian national standard safe level of PM2.5 in the atmosphere over a 24-hour period. The safe level of 25μg/m3 (micrograms per cubic metre) is converted to a sound frequency suitable for human hearing by multiplying 25 by 100, calculating the square root then multiplying the result by 2. This gives us a pitch frequency of 100hz. The 100hz pulsating tone provides a reference point for the listener to compare with the pitch of the modulating tone to determine if atmospheric PM2.5 levels are above or below the safe level. The pulsing speed of this tone will increase and decrease as PM2.5 levels increase and decrease.
Modulating Tone: This tone represents the changing levels of PM2.5 in the atmosphere. The pitch of the tone will change according to PM2.5 levels. The frequency of the tone is determined using the same method as the pulsating tone. The particulate matter sensor detects the level of PM2.5μg/m3 in the atmosphere, which is then converted to sound frequency suitable for human hearing by multiplying the resulting value by 100, calculating the square root then multiplying the result by 2. When PM2.5 levels are considered unsafe, the modulating tone will be higher in pitch than the pulsating tone.
Kick Drum: The rhythm of the kick drum is modulated by amplitude readings of sub 200hz ambient audio in the environment. This frequency range is mostly occupied by the sound of wind and traffic, both which correlate heavily with the small particle density in the air. The amplitude of the audio recording is monitored and is converted to note length by the calculation: length= 0.2*(0.9**(0.4*(ans(dB)-10))). This allows the rate of kick drum pulses to lie within a typical range from 1/32nd notes to 8 whole notes in the tempo 120BPM. A higher amplitude reading thus leads to a faster triggering of the kick drum.
The Recording Process: The set up for the recording of this live sonification involved the use of an outdoor space and an indoor space containing a lit candle and a loudspeaker playing traffic noise at high volume. I begin the recording in the outdoor space where the atmospheric level of particulate matter is low. The slight crackle and the slow pulsing you can hear is caused by the small amount of particulate matter in the surrounding air. You can hear the microphone pick up the occasional bird along with my voice which triggers the pink noise to wash across the stereo field like an ocean wave. I then move to the indoor space where smoke from a lit candle provides a higher particulate matter level than the outside space. This change can be heard in the recording as the crackling sound increases in density, the modulating tone increases in pitch and the pulsating tone speeds up. Pink noise level increases and the low-end kick drum pattern speeds up as the traffic noise from the loudspeaker grows in intensity. The siren-like sound is the dramatic increase in pitch of the modulating tone that has been triggered by smoke from the candle being extinguished. This results in an extreme level of atmospheric particulate matter. I then finish the recording by moving back outside where particulate matter and noise levels are decreased which can be heard in the recording as a sudden drop in pitch of the modulating tone and a decrease in pulsing speed of the pulsating tone. The traffic noise and pink noise also subside as the track fades out.
System Diagrams
Overall flow diagram of project components. 
Converting incoming particle sensor values into an ASCII string with the Arduino and sending it to the Raspberry Pi via the serial port. 
Process of converting ASCII strings into synth arguments to be used as parameter modulators in Supercollider. 
A python script receives live particle data values from Supercollider using the OSC framework to pass on to a html file for display on a webpage.