We study deep bioacoustic event detection through multi-head attention based pooling, exemplified by wildlife monitoring. In the multiple instance learning framework, a core deep neural network learns a projection of the input acoustic signal into a sequence of embeddings, each representing a segment of the input. Sequence pooling is then required to aggregate the information present in the sequence such that we have a single clip-wise representation. We propose an improvement based on Squeeze-and-Excitation mechanisms upon a recently proposed audio tagging ResNet, and show that it performs significantly better than the baseline, as well as a collection of other recent audio models. We then further enhance our model, by performing an extensive comparative study of recent sequence pooling mechanisms, and achieve our best result using multi-head self-attention followed by concatenation of the head-specific pooled embeddings — better than prediction pooling methods, as well as compared to other recent sequence pooling tricks. We perform these experiments on a novel dataset of spider monkey whinny calls we introduce here, recorded in a rainforest in the South-Pacific coast of Costa Rica, with a promising outlook pertaining to minimally invasive wildlife monitoring.
Cite as: Rizos, G., Lawson, J., Han, Z., Butler, D., Rosindell, J., Mikolajczyk, K., Banks-Leite, C., Schuller, B.W. (2021) Multi-Attentive Detection of the Spider Monkey Whinny in the (Actual) Wild. Proc. Interspeech 2021, 471-475, doi: 10.21437/Interspeech.2021-1969
@inproceedings{rizos21_interspeech,
author={Georgios Rizos and Jenna Lawson and Zhuoda Han and Duncan Butler and James Rosindell and Krystian Mikolajczyk and Cristina Banks-Leite and Björn W. Schuller},
title={{Multi-Attentive Detection of the Spider Monkey Whinny in the (Actual) Wild}},
year=2021,
booktitle={Proc. Interspeech 2021},
pages={471--475},
doi={10.21437/Interspeech.2021-1969}
}