Detection and classification of vocal productions in large scale audio recordings

TL;DR

This paper introduces an automated deep learning pipeline for detecting and classifying vocalizations in large-scale, noisy natural audio recordings, effectively handling diverse conditions with minimal labeled data.

Contribution

The novel end-to-end pipeline combines multiple computational techniques to train neural networks on limited data, enabling scalable vocal production analysis in natural environments.

Findings

Achieved over 94% accuracy on two datasets

Processed hundreds of hours of recordings to create new vocal databases

Effective in noisy, real-world recording conditions

Abstract

We propose an automatic data processing pipeline to extract vocal productions from large-scale natural audio recordings and classify these vocal productions. The pipeline is based on a deep neural network and adresses both issues simultaneously. Though a series of computationel steps (windowing, creation of a noise class, data augmentation, re-sampling, transfer learning, Bayesian optimisation), it automatically trains a neural network without requiring a large sample of labeled data and important computing resources. Our end-to-end methodology can handle noisy recordings made under different recording conditions. We test it on two different natural audio data sets, one from a group of Guinea baboons recorded from a primate research center and one from human babies recorded at home. The pipeline trains a model on 72 and 77 minutes of labeled audio recordings, with an accuracy of 94.58% and 99.76%. It is then used to process 443 and 174 hours of natural continuous recordings and it creates two new databases of 38.8 and 35.2 hours, respectively. We discuss the strengths and limitations of this approach that can be applied to any massive audio recording.