Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture.

ABSTRACT: The use of autonomous recordings of animal sounds to detect species is a popular conservation tool, constantly improving in fidelity as audio hardware and software evolves. Current classification algorithms utilise sound features extracted from the recording rather than the sound itself, with varying degrees of success. Neural networks that learn directly from the raw sound waveforms have been implemented in human speech recognition but the requirements of detailed labelled data have limited their use in bioacoustics. Here we test SincNet, an efficient neural network architecture that learns from the raw waveform using sinc-based filters. Results using an off-the-shelf implementation of SincNet on a publicly available bird sound dataset (NIPS4Bplus) show that the neural network rapidly con

SUBMITTER: Bravo Sanchez FJ 

PROVIDER: S-EPMC8333097 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature