Journal
INTERSPEECH 2021
Volume -, Issue -, Pages 471-475Publisher
ISCA-INT SPEECH COMMUNICATION ASSOC
DOI: 10.21437/Interspeech.2021-1969
Keywords
acoustic event detection; deep attention models; multiple instance learning; wildlife monitoring; bioacoustics
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Funding
- Engineering and Physical Sciences Research Council (EPSRC) [2021037]
- DFG (German Research Foundation) Reinhart Koselleck-Project AUDI0NOMOUS [442218748]
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The study focuses on improving wildlife monitoring through deep bioacoustic event detection. By enhancing the audio tagging ResNet and utilizing multi-head self-attention mechanisms, better performance was achieved. The experiments were conducted on a dataset of spider monkey whinny calls recorded in a rainforest in Costa Rica.
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.
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