Classification of all eight classes of vocalizations using different algorithms.

<p>90% of the samples were used for the training set. Time refers to the time required to classify one sample in milliseconds.</p

The effect of training set size on classification performance.

<p>For the sake of visual clarity, the results of OPF using the distance metrics Bray-Curtis and Chi-Square, and SVM using linear and polynomial kernels are excluded.</p

Confusion matrix considering the classification of all eight classes of vocalizations using OPF with Manhattan distance and 90% of the samples for training set.

<p>Confusion matrix considering the classification of all eight classes of vocalizations using OPF with Manhattan distance and 90% of the samples for training set.</p

Vocalization exemplars.

<p>Amplitude and time-frequency spectrograms are shown for representative exemplars of the marmoset call types considered in this study. A: Alarm, B: Chirp, C: Loud shrill, D: Phee-2, E: Phee-3, F: Phee-4, G: Seep, H: Trill, I: Tsik, J: Tsik-Ek, K: Twitter.</p

Confusion matrix for the classification of the principal Tsik class into sub-classes using OPF with Manhattan distance metric and 90% of the samples for training set.

<p>Confusion matrix for the classification of the principal Tsik class into sub-classes using OPF with Manhattan distance metric and 90% of the samples for training set.</p

The number of calls considering each class.

<p>The number of calls considering each class.</p