دانلود گزارش کارورزی و کار آموزی

چکیده :

The ability to identify individuals reliably is often a key prerequisite for animal behaviour studies in the

wild. In primates, recognition of other group members can be based on individual differences in the

voice, but these cues are typically too subtle for human observers. We applied a combined mechanism

consisting of a call feature extraction (mel frequency cepstral coefficients) and pattern recognition algorithm

(artificial neural networks) to investigate whether automated caller identification is possible in

free-ranging primates. The mechanism was tested for its accuracy in recognizing species, call type and

caller identity in a large population of free-ranging blue monkeys, Cercopithecus mitis stuhlmanni, in

Budongo Forest, Uganda. Classification was highly accurate with 96% at the species, 98% at the call type

and 73% at the caller level. It also outperformed conventional discriminant function analysis in the individual

recognition task. We conclude that software based on this method will make a powerful tool for

future animal behaviour research, as it allows for automatic, fast and objective classifications in different

animal species.