# An Agent-Ensemble for Thresholded Multi-Target Classification
## 5 Experiments
### Classification of Ground Vehicles Using Acoustic Signatures  
The acoustic-seismic classification identification dataset (ACIDS) contains acoustic time-series data collected from nine different types of ground vehicles as they pass by a fixed location. An array of three microphones recorded the sound emitted from each passing vehicle. The recordings were made in four different locations including desert, arctic, and mid-Atlantic environments, with vehicle speeds varying from five to forty km/hour, and closest point of arrival (CPA) distances from twenty-five to one hundred meters. The data consists of 274 labeled recordings of CPA events, and Table 6 gives a \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **5** & **10** & **20** & **30** & **40** \\ \hline Prpsd. Gaussian WLR & 0.453 & 0.545 & 0.688 & 0.735 & 0.754 \\ Prpsd. Platt WLR & **0.503** & **0.597** & **0.761** & **0.821** & **0.846** \\ Joint Gaussian & 0.358 & 0.534 & 0.698 & 0.775 & 0.798 \\ Ind. Gaussian & 0.435 & 0.548 & 0.691 & 0.745 & 0.756 \\ AC Gaussian WLR & 0.447 & 0.544 & 0.688 & 0.735 & 0.754 \\ AC Z-norm & **0.507** & 0.552 & 0.662 & 0.699 & 0.711 \\ AC F-norm & 0.452 & **0.610** & **0.751** & **0.821** & **0.841** \\ AC EER-norm & 0.237 & 0.338 & 0.572 & 0.732 & 0.729 \\ Meta-SVM & 0.452 & **0.568** & 0.680 & 0.771 & 0.820 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average PAUC for the Yale Faces pin-less verification experiment. Column titles give the number of training examples from each client person. Boldface identifies results that are the best or statistically significantly tied for the best with 95% confidence. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **5** & **10** & **20** & **30** & **40** \\ \hline Prpsd. Gaussian WLR & 95 & 97 & 98 & 98 & 99 \\ Prpsd. Platt WLR & 97 & 98 & 99 & 99 & 99 \\ AC Gaussian WLR & 95 & 97 & 98 & 98 & 99 \\ AC Z-norm & 97 & 98 & 99 & 98 & 98 \\ AC F-norm & 95 & 97 & 98 & 99 & 99 \\ AC EER-norm & 82 & 92 & 96 & 99 & 99 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average percent accuracy when using the maximum normalized agent-output to estimate the client that causes an alert when the threshold is set to give five percent probability of false alarm (PFA). Column titles give the number of training examples from each client person. breakdown of the number of events for each type of vehicle. The Army Research Lab collected the data and made it available [31]. We use the acoustic data from microphone one only. The sampling frequency is 1025.641 Hz and the data is bandpassed between twenty-five and four hundred Hz. We pre-process the data and extract features in a manner that matches closely that of a previous study using the ACIDS dataset by [PERSON] and [PERSON] [32]. We first estimate the CPA for each time-series. We do this by filtering the magnitude of the acoustic response with a two-second moving-average filter and estimating CPA as the point where the output achieves its maximum value. We then limit the data to a forty-second window centered on the CPA. We convert the windowed time-series into a spectrogram by taking the short-time Fourier transform with a one-second window, frequency resolution of one Hz, and fifty percent overlap. This gives a spectrogram that contains eighty time-scans and 376 frequency bins. We pass the spectrogram through a spectrum normalizer to remove the broadband energy trend, so that the end result is a spectrogram that characterizes the narrowband content of the vehicle's acoustic signature. The normalized spectrogram for the first CPA event in the ACIDS dataset is shown in Figure 3.  
We featurize the spectrograms using the magnitude of the spectrum at the second through twelfth harmonics of the fundamental frequency as was done in [32]. To get these features, we first estimate the fundamental frequency of the predominant harmonic set in each spectrogram that, as described in [33], typically relates to the engine cylinder firing rate.