using comfort level 1 10 as predictor to replace temperature chu qian to dr wayne daley n.
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Using comfort level 1-10 as predictor to replace temperature chu qian to Dr. Wayne Daley. Temperature-->Comfortable.

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using comfort level 1 10 as predictor to replace temperature chu qian to dr wayne daley

Using comfort level 1-10 as predictor to replace temperaturechuqianto Dr. Wayne Daley

temperature comfortable
Temperature-->Comfortable
  • Because sometimes temperature as the predictor can be very indirect, we use the tool “comfort level from 1-10” to substitute this measurement. This makes our results more self-explanatory
  • Temperature range: 67.66 - 89.1 deg-F
  • Temperature is divided according to this:
  • each
slide3

We assign this comfort level according to previous experimental results: for adult chicken (>28 days), the best temperature for growth is 20-20 deg-C, which is about 67.66-69.84 deg-F

  • And we assume that the higher the temperature, the worse the chicken will be feeling, thus the more stressed the environment.
spectrogram analysis
Spectrogram analysis

Using ‘20100103 1259.flac’ as an example, this flac file is 55 sec long:

  • function [S, F, T, P]=spectrog(fn)
  • n = 1024;
  • numoverlap = 0;
  • window = hanning(n);
  • [sound_data,Fs]=audioread(fn);
  • sound_data=sound_data(1:end,1);
  • [S, F, T, P]=spectrogram(sound_data,window,numoverlap,n,Fs);
  • end
slide6
Note
  • This audio flac file is 55 s long. We set our time overlap to be 0 (no overlap) and Matlab“automatically” gives a time increment of 0.0213 s, which gives us 55/0.0213=2592 time slots
  • In other data files which are 1 min long, the time increment is still 0.0213 s, hence this value can be thought of as a Matlab constant
  • Also the frequency increment is 46.78 hz
  • Half sampling rate=24000:
  • 24000/46.79=513 frequency slots
divided into four sub bands
Divided into four sub-bands
  • Band1: [0,3000]
  • Band2: [3000,6000]
  • Band3: [6000,12000]
  • Band4: [12000 24000]
  • Unit: Hz
  • The reason that we divide it into four bands is arbitrary, right now we are experimenting 4 sub-bands as suggested in the paper “content-based audio classification and retrieval using a fuzzy logic system: towards multimedia search engines”
to get the sub band energy ratio
To get the sub-band energy ratio
  • After dividing the frequency matrix into 4 sub-bands, we sum up the power in each band and divide that by the total energy in the entire frame to get the sub-band energy ratio.
  • Since we are summing it up across this 1min period, each small time slot does not matter to us, we are looking at the big picture of the sub-band energy ratio of an entire 1 min flac file
slide9

trainingSetReal.xlsx(containing ratio information)

  • We use these 240 points to train our system, in addition:
  • numMFs=3;
  • mfType='gbellmf';
  • epoch_n=20;
  • We get a new fis system specComUnfiltered3.fis that contains 81 rules:
  • 4 inputs
  • 3 membership functions
  • 3^4=81 rules
  • This is a very big number of rules, it requires about 2 min computational time on Georgia Tech library equipment
slide10
`
  • evalSet.xlsx
  • We have 3 abnormaliesamoung 240 evaluated data:
  • -11.7482
  • --3.1471
  • 73.9784
  • Everything else are in a good range around 1-10
  • Since the amount of abnormaliesare extremely small compared with our population, we can discard these three points for next step
group mse
Group mse

This spectrogram analysis gives us a prediction similar to the amplitude analysis: “sub-band energy ration of chicken vocalization as a feature of stress level works very well (with mean square error of 0.386913) under low-stress environment, but produces wider ranges of fluctuation (with MSE 30 times of that in unstressed condition) under high stressed ones.”

average amp vs spectrogram
Average amp VS. spectrogram

As a result, we believe that spectrogram analysis yields a little bit better prediction for the comfort level, although the difference is not very significant.

To test the difference between this two methods, we can use a 2-sample variance test. Useful tool for this is Minitab.

Null hypothesis: var1=var2

Alternate hypothesis: var1 =! Var2 (not equal to)