Deep learning techniques for biological signal processing: Automatic detection of dolphin sounds

openConsidering the heterogeneous underwater acoustic transmission context, detecting and distinguishing vocalizations of cetaceans has been a challenging area of recent interest. A promising venue to improve current detection systems is constituted by machine learning algorithms. In particular, Convolutional Neural Networks (CNNs) are considered one of the most promising deep learning techniques, since they have already excelled in problems involving the automatic processing of biological sounds. Human-annotated spectrograms can be used to teach CNNs how to distinguish between information in the time-frequency domain, thus enabling the detection and classification of marine mammal sounds. However, despite these promising capabilities machine learning suffers from a lack of labeled data, which calls for the adoption of transfer learning to create accurate models even when the availability of human taggers is limited. In this thesis, we developed a dolphin whistle detection framework based on deep learning models. In particular, we investigated the performance of large-scale pre-trained models (VGG16) and compared it with the performance of a vanilla Convolutional Neural Network and several baselines (logistic regression and Support Vector Machines). The pre-trained VGG16 model achieved the best detection performance, with an accuracy of 98,9\% on a left-out test dataset.Considering the heterogeneous underwater acoustic transmission context, detecting and distinguishing vocalizations of cetaceans has been a challenging area of recent interest. A promising venue to improve current detection systems is constituted by machine learning algorithms. In particular, Convolutional Neural Networks (CNNs) are considered one of the most promising deep learning techniques, since they have already excelled in problems involving the automatic processing of biological sounds. Human-annotated spectrograms can be used to teach CNNs how to distinguish between information in the time-frequency domain, thus enabling the detection and classification of marine mammal sounds. However, despite these promising capabilities machine learning suffers from a lack of labeled data, which calls for the adoption of transfer learning to create accurate models even when the availability of human taggers is limited. In this thesis, we developed a dolphin whistle detection framework based on deep learning models. In particular, we investigated the performance of large-scale pre-trained models (VGG16) and compared it with the performance of a vanilla Convolutional Neural Network and several baselines (logistic regression and Support Vector Machines). The pre-trained VGG16 model achieved the best detection performance, with an accuracy of 98,9\% on a left-out test dataset

Automated Detection of Dolphin Whistles with Convolutional Networks and Transfer Learning

Effective conservation of maritime environments and wildlife management of endangered species require the implementation of efficient, accurate and scalable solutions for environmental monitoring. Ecoacoustics offers the advantages of non-invasive, long-duration sampling of environmental sounds and has the potential to become the reference tool for biodiversity surveying. However, the analysis and interpretation of acoustic data is a time-consuming process that often requires a great amount of human supervision. This issue might be tackled by exploiting modern techniques for automatic audio signal analysis, which have recently achieved impressive performance thanks to the advances in deep learning research. In this paper we show that convolutional neural networks can indeed significantly outperform traditional automatic methods in a challenging detection task: identification of dolphin whistles from underwater audio recordings. The proposed system can detect signals even in the presence of ambient noise, at the same time consistently reducing the likelihood of producing false positives and false negatives. Our results further support the adoption of artificial intelligence technology to improve the automatic monitoring of marine ecosystems