Michael Lee

Jeff: Was this a direct transcription of the reversed audio or from a microphone picking up the ambient sound + speaker playing the reverse audio?

 In any case, I'm OK with software as sensitive as this type of transcription because you can use other metrics like word counts to verify if the text is random or coming from a spiritual source.

Having a speech-to-text like is very useful. I'm not quite ready to pay for the monthly subscription, but I appreciate your testing. Regarding the audio, it sounds like a feedback echo may be occurring, where your microphone is picking up from the speaker. Anytime I use a microphone, I wear headphones. Even then, a sensitive enough microphone can hear headphones

Sharon,

Sorry your post didn't get responded to earlier. Must be the universal interference  Yes, it's consistent with what we know about spirit communication that it can be enhanced when our energies are activated. I can try to enhance the speech, too.

-michael

Fernando, I like that you are thinking orthogonal to my time-based approaches. I had a dream a few days ago that someone had wrote a successful "Morse code" ITC software. My gut feeling is that spirits can manage time pretty well, but maybe Im wrong. Either way, good luck!

This reminds me of my tone vocoders. One of the challenges is ensuring that each tone has the same statistics (fluctuations in volume). I also can develop machine learning models for whatever set of tone frequencies you choose that will "translate" to clearer voice.

Hi Robert!

The quantum selection effect could be a mechanism of how spirit influences things (electronics and our physical bodies). If so, one would want to prepare as many metastable states as possible for the spirits to influence. An analogy is the spirit knocking over bowling pins. One could count the number of pins falling over per unit time or the measure direction they fell. 

One of the biggest problems I've noticed, and I've explored not just audio, but images, and digital ITC, is how weak the spirit signal actually is. This means that we need sort of a paradoxical situation. Quietness from the physical environment but also metastability/ease of knocking over. Typically, sensitive sensors pick up everything around them.

Over the last few months, Michael Lee of the Varanormal research team has developed and distributed a new software program for Windows called Spiricam. This program takes the input of your webcam and processes data in-between frames into features that sometimes resemble natural looking objects like faces. Watch a demo video. Also, download the program and try for yourself.

On 11/1/2021 at 11:51 AM, Fernando Luis Cacciola Carballal said:

Hi Michael,

This program is awesome!

And the presentation Friday night was great.

I've found a sort of small unwanted feature (we programmers don't say the word bug): if you change the device after it started, it won't do anything. You have to stop it first, then re-start it.

Other than that it works like a charm. Haven't see anything yet, but, I'll keep fishing.

 

 

The new version v0.81 automatically pauses the recording, if a new camera is selected during recording.

On 10/31/2021 at 10:07 AM, Paranormal Reason said:

Hi Michael, I downloaded V 0.74 yesterday, but I've just unzipped the files and gone to install it when I got hit with the usual warning message - it's telling me that it contains the HEUR/APC Malware threat.

Has anyone experienced this before and is it a known false flag, or is it something else?

I'm running Windows 10 and, am using Avira Security. Not sure if any additional information will be of help to you?

I've popped the file in quarantine for the moment, any help or guidance you could provide would be gratefully received.

Kind Regards
Mark D.  

Some malware detectors don't like Python executables and all of the libraries. From what I've Googled, HEUR=heuristic - it's guessing that one of the libraries might be evil. But people say it shouldn't be a problem.

Sincerely,

the Malware on Michael's computer. 

2 hours ago, Andres Ramos said:

Maybe this exactly is what Bigelow wanted. Still I'm not quite clear about the intention of this contest or why the world is a better place now after Bigelow did it.

Here's hoping the next $1 million is devoted to a call for research proposals in the paranormal / ITC field. Maybe I'll finally be able to purchase a zero Kelvin quantum camera .

Interesting just looking at the table of contents of the top winner's essay. It reminds me of Victor Zammit's work, basically, a "Best-Of" compilation of afterlife evidence.

Welcome!

In my opinion, ITC, for now, walks a fine line between real and imagined because the signals that we are trying to discern are often very weak. 

Feel free to share with us any of your results and ideas on the forum.

Having broken free of audio ITC (for now), I've tried some visual ITC and developed Spiricam https://www.varanormal.com/forums/topic/1121-spiricam-combining-camera-input-and-a-touch-of-image-processing/?do=getNewComment.

But if you really want to start quantifying whether spirit is having an effect on ITC, digital ITC is both the most rigorous and most difficult to figure out.

I've tried a few strategies over the years, but I've converged on the idea of expecting a certain rate of symbols, such as 1 symbol per second, and using some reasonably random (but physical) noise source - for example, the line-in input of a laptop.

In a previous post,  I mentioned a parity test, where we are receiving 0s and 1s, but the number of 1s should be even per 8-bits/1 byte. This would be a "parity" or error check mechanism. Of course, a more complicated scheme could be used, but we'll start with this. 

Now as far as encoding characters, I'm opting at first to try ASCII, which is a standard 7-bit set (0-127) used in the US, but has most of the common symbols of English, the 10 digits and some punctuation and math symbols.

How to convert physical noise into bits. There's many strategies out there. I want one that achieves some amount of randomness without forcing it and losing information that might be coming through.

If you listen to the line-in audio, with nothing connected, it predictably sounds like noise + ground hum. So, I've decided to apply spectral subtraction, which should remove any periodic tone interference and also a layer of physical noise.

Then, if I'm collecting one bit per 125 ms (8 bits per second), I sum up the processed audio samples for each bit, if it's above zero, I call the bit a "1" otherwise the bit is a "0".

Here's a snippet of results, so far. I just started running it an hour ago:

,$,b,..,Z,,,,.,,,,,H 0.511
,,,.,Du2,,,Zm,Mw&&,, 0.513
==,,J.,,>,,1|,U(gT,, 0.515
#.,,],\,bf..,444,,., 0.518
,`C+,,.,2V,.^,S,,,,, 0.515
,,,,#,j,,,oV.D,,,,,M 0.511
.VT, N)lZn,1, ,,,,,, 0.512
,.,Q,.,JP-.y.n, ,,., 0.516
,9,,,eR,..,,,V,.,,,, 0.510
,.,,.-,u@|u,;{.O;,j, 0.515
,g ,m,k,15v,,Og,|,Iu 0.517
#,.{,j,.,Y,,.,{!,{m. 0.520
.],,,=.N,,`,fB,,,.,, 0.518
8,,),.,.,,.,,.,,,;.- 0.516
i.8.,,/..oCT.,,,G,,, 0.518
,=/,.,,to,,,,>.,Z(,Y 0.518

The right number column gives a running tally of fraction of bytes that have even parity.

The symbol "." is used to represent the first 32 symbols, which are not displayable.

The symbol "," is used to represent bytes that have odd parity, and therefore, we want to exclude.

And here's the Python code:

# -*- coding: utf-8 -*-
"""
@author: Michael S. Lee (10/30/2021)
"""

import numpy as np
import pyaudio
import time
import keyboard
from scipy.signal import stft, istft

# Convert byte value to character
def convert(byte):
  if ((byte < 32) or (byte == 127)):
      char = '.' # invisible character
  else:
      char = chr(byte)
  return(char)
  
def callback(data, frame_count, time_info, status):
  
  global index, bias, line, parity

  # Extract frame of audio from pyaudio buffer     
  frame0 = np.copy(np.frombuffer(data,dtype='float32'))
  
  # Normalize audio
  frame = 0.05 * (frame0-frame0.mean()) / frame0.std()
  
  # Simple spectral subtraction to remove interference and noise
  f,t,F = stft(frame, nperseg = 256)
  G = np.abs(F)
  G1 = np.copy(G)
  P = F / (G + eps)
  mean = G.mean(axis=-1)
  for ii in range(G1.shape[1]):
      G1[:,ii] = np.maximum(G1[:,ii] - mean*spec,0.0)
  t, frame1 = istft(P*G1)
  
  # Normalize audio
  frame1 = 0.05 * (frame1-frame1.mean()) / frame1.std()
  
  bit = np.zeros((8),dtype='uint8')
  for i in range(nbits):
      block =  frame1[i*nblock:(i+1)*nblock]
      bit[i] = (block.sum() > 0) * 1
     
  # Save character only if parity is even
  if (np.mod(bit.sum(),2)==0):
    byte = bit[0] + bit[1] * 2 + bit[2] * 4 + \
           bit[3] * 8 + bit[4] * 16 + bit[5] * 32 + \
           bit[6] * 64
    char = convert(byte)
    parity = parity + 1
  else:
    char = ',' # parity was odd

  #bias = lambda1 * bias + (1.0 - lambda1) * (frame1.sum() / 8.0)
  #std = frame1.std()
 # print (bit, bit.sum(), "%5.2E" % bias, "%5.2E" % std)
 
  # Print and add next character
  print (char, end="") 
  line = line + char
  index = index + 1
  
  # Save line to text file
  if (np.mod(index, nchar) == 0):
      frac = '%4.3f' % (parity / index)
      save_file.write(line+" "+frac+"\n")
      print (" "+frac)
      line = ""
  
  # Play sound of spectrally subtracted audio
  # can be multiplied by zero to be quiet
  data2 = np.ndarray.tobytes(frame1 * 0.0)
  return (data2,pyaudio.paContinue)

# Sampling rate
fs = 16000

# Audio block size, could be a second, 
# could be shorter/longer.
nframe = fs
print (nframe,'samples per frame')
print ("------------------------")

# Index of all frames
index = 0

# #bits per frame
nbits = 8

# Bias - starting value
bias = 0.0

# lambda1 - update rate of bias
lambda1 = 0.9

# strength of spectral subtraction
spec = 1.75

# block size - needs to be integer
nblock = nframe // nbits

# #chars per line
nchar = 20

# small number
eps = 1e-8

# Accumulating line of characters
line = ""

# Keep track of parity fraction
parity = 0

# Audio device index
microphone = 3
output = -1

# Give file a unique name
recordfilename = time.strftime("%Y%m%d-%H%M%S")+'.txt'

# Text file to store stream
save_file = open(recordfilename,'w')

# Start audio stream
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paFloat32,
                channels=1,  # mono
                rate=fs,
                output=True, # We're just generating text
                input=True,  # Audio input source
                input_device_index=microphone,
                output_device_index=output,
                frames_per_buffer=nframe, # 1-second
                stream_callback=callback)

# Wait for key press
while True:
    if keyboard.read_key() == "q":
        print("\nUser break.")
        break
    time.sleep(1)

# Close stream and text file
stream.close()
save_file.close() 

I've posted the latest version in the Downloads section. 

Here's a video demo/tutorial for the Spiricam software (v.0.73):

Fernando, I'm working on a new version. I should have something for you by this weekend. Fewer (newer) bugs and some more features.

Kevin thanks for stepping up to the plate. If the program actually runs, I think you will enjoy it, and so will your team. 

I've put a graphical interface onto my Spiricam program and managed to package it with pyinstaller, so it can run as an executable (amidst a ~1GB sea of support files).

If anybody has a Windows PC with a webcam (or one they can attach), and wants to experiment with visual ITC, let me know.

Here's a screenshot of the program:

The program has a few machine learning models to reverse things like additive noise, and quantization artifacts. Also, it has a primitive "speech model" that adds formants it finds to a "buzzing" glottal pulse. The machine learning models were trained on a 30 min speech of Gen. David Petraeus speaking to Congress, so it's funny when German is trying to be spoken through it.

We'll try to get you "hooked up" with the program. It requires a good CPU. It requires a few steps to install, including Anaconda or Miniconda (A Python environment), and may take about 5-10 GB of disk space - not because my program is particularly large, it's just that Python and end-users don't mix

For random number sequences, the frequency means the relative differences between neighboring numbers. The sequence of numbers 3, 95, 26, 62 is bouncing around (high frequency). The seq. 25, 32, 28, 27 is less jumpy (low frequency).

Now imagine pixel neighborhoods. Blurring makes all the pixels almost the same in a neighborhood. But to get finer features, you need a balance: a little bit of blurring at each length scale: neigborhood, village, city, county, state, etc.

5 hours ago, Karyn said:

Interesting pics Michael.  I would love to see the two bottom ones upside down.

The wants plan took a hit this week. I would buy myself a couple of misting machines and convert the camera to a tripod or some other stable stand. The plan remains a plan now, seriously taking a hi.  It stays put back another couple of weeks/month.

Inka ~ pussy cat, which I have now held more than I had the opportunity to keep my Em, had a super expensive operation day before yesterday. So once again, my playthings slide down the wants pile. The Needs pile has grown.

Karyn, the beauty of this new technique is it doesn't require mist, smoke, or mixing cleaning solutions (chemistry joke). We all have a webcam and a computer. I also envision a cellphone app. The software converts the digital noise and the light particles in a room into "smoke." It is like we can now see the invisible air itself.

5 hours ago, Andres Ramos said:

Interesting. So the idea is to provide the outline of a face as a base for, lets say information precipitation, that helps the spirits to do their manipulations at the right points but in the end forming another face that is not yours?

In that case wouldn't it be favorable to use a somehow "neutral" face like a smiley?

Spiricam is a general approach. It works just fine with a blank wall or in a closed box. Pointing at someone is a variation that might reveal something about their aura. To be on the safe side, one could put a sheet over their face.

In the next pictures, the first three are just me with the distortion of the method, the camera pointed at me.

The next one looks a little like me. #5, #6, and #7, however, showed up over my face and don't look anything like me.

The final one is just one I found elsewhere in the smoky pictures.

Building on my work with Perloids derived from the noise of software-defined radios, I decided to explore looking at the noise coming from cameras. In principle, camera provide high-bandwidth information and spirits could potentially either interact directly with individual pixel-level sensor elements or with the light in the environment.

The two "tricks" then for getting things to work is to derive as much noise as possible from the camera, and then apply our favorite transformation that either blurs or "smokifies" the noise.

How do you get lots of noise from a camera? Well, one way would be turn up the gain and exposure time, and put the camera in complete darkness. Another more universal method is to simply subtract the current frame from the previous frame. This allows us to point the camera at virtually anything (or nothing at all) and get a noisy pattern.

Here's an example from a webcam. I've mathematically amplified the noise so you can see it. 

Now the next trick is to somehow make heads or tails of the noise. Blurring is one possibility, which could be done with something called Gaussian kernel convolution in image manipulation programs like GIMP and Photoshop. However, even better than that is "smokification." That's a word I just made up, but it's like how Perlin noise is created from regular white noise. White noise is fairly featureless and looks like indistinguishable sand, but Perlin noise often produces cloudy / smoky-like textures like the ones you see in Keith's Perlin image experiments.

Now, I've been trying out different variations on Perlin, like my so-called "Perloids" but the general range of useful ones seem to be between "inverse frequency" and "inverse squared frequency" What this means in laymans terms is that we try to transform noise to look as real as possible using essentially a two dimension "graphic equalizer" (you know, the audio version for controlling high, mid, and low frequencies?)

Now imagine amplifying the "bass frequencies" (or the largest features of an image) and keeping the mid-levels in the middle, and dampening out  the "treble/high frequencies" (the smallest features). This is the essence of Perlinizing / smokifying noise - and the results are amazing as we've seen with Perlin noise images, and continue to be with my webcam/noise input.

Incidentally, the original Varanormal France Perlin program uses random numbers generated with a deterministic algorithm, although a non-deterministic seed. How spirits figured out how send messages through that channel puzzles me to this day, but it seems to work. Here I'm using physically-generated noise (from a webcam), so at least it seems plausible that spirits could influence the electronics or the light in the air to send us information.

And now for some pictures. Caveat emptor: I can't prove that anything we see here is really from spirit, and not just active imagination. But I'm hoping over time, the proof will either get clearer, or we'll have too many instances to dismiss.

The first two are from a camera I got in the mail today, and they're a bit disappointing. But what you might see in black and white are four faces followed by one face. The next four small ones are faces. The final one on the 2nd row appears to have at least four faces. 

The last row is a little different. The first one is a whole body image. The second looks like a humanoid-alien face. The final one is an example of when I point the camera at myself and hold real still. Maybe we'll start being able to view the personalities of our soul?

One very last comment for this post. There's no machine learning tricks in the method (yet?!?), just "simple" math.

Today's "2 minute" video.

Here's what I found: