## 3 Experiments
### Chicken (Gallus Domesticus): Age-Dependent Stress Condition Classification  
Maintaining low-stress living conditions is extremely important to the care of domesticated poultry used for agricultural food production. However, currently the only clinical tool for directly measuring stress levels requires measuring hormonal indicators in blood samples, which itself stresses the  
\begin{table}
\begin{tabular}{c c} \hline
**Measure** & **Accuracy** \\ \hline Song Type & 100\% \\ Syllable & 97.5\% \\ Song Variant & 77.6\% \\ \hline \end{tabular}
\end{table}
Table 3: Song type, syllable, and song variant classification accuracy for overall system.  
\begin{table}
\begin{tabular}{c c} \hline
**Methodology (15 state HMMs with 1 Gaussian)** & **Syllable** \\  & **Accuracy** \\ \hline Baseline 39 element feature vector & 61.0\% \\ Baseline + CRN (_Constrained Recognition Network )_ & 74.0\% \\ Baseline + CMS (_Cepstral Mean Subtraction_) & 88.0\% \\ Baseline + CRN +CMS + SE (_Signal Enhancement_) & 95.9\% \\ Baseline + CRN + CMS + SE + SP (_Short Pause_) & 96.7\% \\ Baseline + CRN + CMS + SE + SP + EN (_Energy Normalization_) & 97.5\% \\ \hline \end{tabular}
\end{table}
Table 4: Increase in accuracy due to sequential system improvements. Constrained Recognition Network (CRN) refers to the song-type language model of Figure 3b. Cepstral Mean Subtraction (CMS) is feature normalization as described in Section 2.2. Signal Enhancement (SE) is Bandpass plus Ephraim-Malah filtering as described above. Short Pause (SP) is the song-type language model of Figure 3c, and Energy normalization (EN) is baseline curve energy normalization as described above.  
animals. This study is focused on investigating the correlation between vocalization patterns in agricultural chickens and various stress stimuli in this environment, for the purpose of assessing whether vocalizations could be reliably used as a stress indicator. Two separate stress-related task experiments are implemented, one focused on directly detecting living condition stress in vocalizations and one focused on evaluating the connection among stress induced by human presence, diet and age.  
Vocalizations were collected at the University of Connecticut Poultry Barn, which houses 25 animals. A stationary microphone to which the animals were accustomed was used. Long (multiple-minute) DAT recordings at a 44.1 kHz sampling rate were made after acclimatization to each condition under test, and then divided into 10 second duration segments for analysis and classification. The vocalizations used in these experiments vary from previous studies in that there are large numbers of individuals vocalizing simultaneously, so that individual vocalizations cannot be separated and there is no discernible time patterning. This is accommodated within the HMM framework by reducing the number of states to 1 because there is no time sequence to be modeled, and increasing the number of mixtures to 12 to get a finer resolution discrimination in terms of overall spectral characteristics across the population. Thus, the classification method is spectral as opposed to sequential, and the HMM approach becomes equivalent to a much simpler GMM statistical classifier.  
Feature extraction is again similar to that of the previous tasks, using a base 39-element GFCC feature vector, with Hamming frames of 25 ms each, frame overlap of 15 ms, and 26 filter banks for analysis. The lower and upper frequency limits are set to 125 Hz and 2,000 Hz [47], respectively, based on the approximate hearing range. However, the log energy coefficient is not included since there is no need for a loudness pattern due to the non-temporal model, and to avoid any bias that might be introduced by overall vocalization amplitude in different conditions,