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- W2950243585 abstract "ABSTRACT We introduce an end-to-end feedforward convolutional neural network that is able to reliably classify the source and type of animal calls in a noisy environment using two streams of audio data after being trained on a dataset of modest size and imperfect labels. The data consists of audio recordings from captive marmoset monkeys housed in pairs, with several other cages nearby. Our network can classify both the call type and which animal made it with a single pass through a single network using raw spectrogram images as input. The network vastly increases data analysis capacity for researchers interested in studying marmoset vocalizations, and allows data collection in the home cage, in group housed animals." @default.
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- W2950243585 date "2018-10-07" @default.
- W2950243585 modified "2023-10-17" @default.
- W2950243585 title "Deep Convolutional Network for Animal Sound Classification and Source Attribution using Dual Audio Recordings" @default.
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- W2950243585 doi "https://doi.org/10.1101/437004" @default.
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