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Introduction to Hardware Noise sources


Michael Lee

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Pre-built Electronics

The first noise sources I worked with were generated by pre-made electronics: the USB input audio interface turned up to max gain (+46 dB) and a software-defined radio tuned to no radio station/source. Both of these sources produce nearly white noise. White noise means that all of the frequencies are the same magnitude.

Both of these sources are probably suitable for noise-gate applications like the phonetic keyboard. However, in order to derive voice directly from noise, I have often hypothesizes that  we would need something more sophisticated.

Home-made Electronics

Years ago, I avoided getting into ITC precisely because I didn't feel I had the chops to make electrical ITC circuits that people prescribed. Only about two years ago, did I realize that ITC is as much a software problem as a hardware one, and pre-built noise sources might be sufficient. However, I wanted to go further with hardware noise (entropy) sources.

I'm very cautious when it comes to electronics. I'm not interested in working with high-power systems because I don't want to start any fires or shock myself. I don't own (yet) a 30V DC adjustable power supply (which BTW, often has a lot of annoying periodic interference noises). So my main two main sources of power, to-date, are a 3 x AA battery (4.5V) power supply and the 48 V phantom power from my USB audio interfaces and mixers. Phantom power is very low current and thus fairly safe, but it can't power too much circuitry.

Reverse-biased White LED

One of my earliest hand-made noise sources, which I discovered by accident, but is commonly known, is the reverse-biased light emitting diode (LED). If you apply >30V to a small white LED, you will often, but not always, get pink noise that can be made quite audible with the 200x (46 dB) gain of a USB audio interface. Simply, put a 100 kilo-ohm resistor in series with +48V, pin 2 of an XLR connected to the microphone interface. That resistor hooks to the cathode of the LED. The anode is then connected to ground (XLR Pin 1). Every single LED has different noise characteristics, but if you try, say 10 LEDs, you should find one or two that produce a distinct grumbly pink noise. I've since bought 100's of white LEDs, and find about 30% make good noise. Some LEDs are louder than others. The thinner LEDs tend to work better, but YMMV.

The phenomenon yielding noise in this setup is known as the avalanche breakdown effect. In layman's terms (and my primitive understanding), the high voltage running in the opposite direction of normal operation for the LED, causes the current to spill over, in a non-deterministic fashion. If you look closely on an oscilloscope, you can sometimes see a random sawtooth pattern. The energy builds up and then randomly collapses producing flicker / pink noise.

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Is it possible spirits can control when the, otherwise random, spill points happen? Nonetheless, the lower triangular power spectrum of pink noise somewhat resembles the spectrum of human speech. Human speech starts at around 75 Hz, build up to 300-500 Hz and then decays to 5-6 kHz with a slight bump near the high end for sibilants like "s" and "t".

Reverse-biased NPN transistor

A similar avalanche effect, and similar setup, can be achieved with a transistor. My favorite device, which I've also bought 100's of, is the N2222(A) NPN transistor. At around 10V, with a reverse bias between the base and emitter leads, white noise can result. This noise, I originally simply amplified, again, with the (up to) 200x gain of my USB interface.

Arrays of Reverse-biased PN junctions

The results for the avalanched white LED for direct voice often sounded a little better than what I could get for the avalanched transistor, however I still wanted a better signal-to-noise ratio (SNR). A common method for improving SNR is to use more identical sensors and sum up their signal. The concept is that if the signal is the same in each sensor, it will grow in amplitude linearly with the number of sensors, N. However, if the noise in each sensor is uncoupled, then it should it only accumulate as the square root of N. In total, the SNR should grow as the square root of N. 

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(Illumination for fun. In reverse-biased mode, LEDs don't light up.)

Maybe in sensors for physical phenomena, this is true, but for picking up spirit signals, it never quite works as well. To be sure, an array of 30-50 avalanched circuit elements produces a better signal than a single element, but 100 or 400 elements in parallel doesn't seem to make much difference. 

There are a few reasons why this could be the case:

1) The noise in each element is not completely uncorrelated from each other. If the noise were say ground hum, this would make sense. However, the noise often appears to be random, not interference from other electronic systems in the environment.

2) Spirits can't equally affect all sensors at once. Interestingly, they often use the term "field" to describe my arrays of LEDs and transistors.

3) The phantom power gets drained too much from powering multiple elements. This effect can be ameliorated by using more than one phantom power source. For example, I have a cheap 8 8 x XLR mixer that I've tried. Another trick is to up the resistor from 100K to 1M. The downside is the loudness per device is reduced.

4) After averaging all of the additive noise, there are still other degradations that can't be simply averaged out. This would be true, for example, if spirits were actually modulating the noise to produce voice. In this case, inference algorithms would be needed to "clean up" this effect.

A white LED array powered by 48V phantom power from a USB audio interface. Notice the lone green LED - other color LEDs can sometimes also generate pink noise.
 

Massive Arrays

Some spirits think that if we could get a few 1000 LEDs in parallel, we would reach better clarity. I did buy something called an Avalanche Photodiode (APD). This device contains 4000 or so diodes, and it's job is to detect single photons. The white noise produced sounded very smooth, but the ML-translated spirit voices weren't necessarily that much better. So for now, large arrays for better vocal clarity? The jury is out. 
 

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