Improve automatic detection of animal call sequences with temporal context.

ABSTRACT: Many animals rely on long-form communication, in the form of songs, for vital functions such as mate attraction and territorial defence. We explored the prospect of improving automatic recognition performance by using the temporal context inherent in song. The ability to accurately detect sequences of calls has implications for conservation and biological studies. We show that the performance of a convolutional neural network (CNN), designed to detect song notes (calls) in short-duration audio segments, can be improved by combining it with a recurrent network designed to process sequences of learned representations from the CNN on a longer time scale. The combined system of independently trained CNN and long short-term memory (LSTM) network models exploits the temporal patterns between son

SUBMITTER: Madhusudhana S 

PROVIDER: S-EPMC8292017 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature