In the last few years, automatic extraction and classification of animal vocalisations has been facilitated by machine learning (ML) and deep learning (DL) methods. Different frameworks allowed researchers to automatically extract features and perform classification tasks, aiding in call identification and species recognition. However, the success of these applications relies on the amount of available data to train these algorithms. The lack of sufficient data can also lead to overfitting and affect generalisation (i.e. poor performance on out-of-sample data). Further, acquiring large data sets is costly and annotating them is time consuming. Thus, how small can a dataset be to still provide useful information by means of ML or DL? Here, we show how convolutional neural network architectures can handle small datasets in a bioacoustic classification task of affective mammalian vocalisations. We explain how these techniques can be used (e.g. pre-training and data augmentation), and emphasise how to implement them in concordance with features of bioacoustic signals. We further discuss whether these networks can generalise the affective quality of vocalisations across different taxa.

Bioacoustic classification of a small dataset of mammalian vocalisations using deep learning / Manriquez P, R.; Kotz, S. A.; Ravignani, A.; de Boer, B.. - In: BIOACOUSTICS. - ISSN 0952-4622. - 33:4(2024), pp. 354-371. [10.1080/09524622.2024.2354468]

Bioacoustic classification of a small dataset of mammalian vocalisations using deep learning

Ravignani A.;
2024

Abstract

In the last few years, automatic extraction and classification of animal vocalisations has been facilitated by machine learning (ML) and deep learning (DL) methods. Different frameworks allowed researchers to automatically extract features and perform classification tasks, aiding in call identification and species recognition. However, the success of these applications relies on the amount of available data to train these algorithms. The lack of sufficient data can also lead to overfitting and affect generalisation (i.e. poor performance on out-of-sample data). Further, acquiring large data sets is costly and annotating them is time consuming. Thus, how small can a dataset be to still provide useful information by means of ML or DL? Here, we show how convolutional neural network architectures can handle small datasets in a bioacoustic classification task of affective mammalian vocalisations. We explain how these techniques can be used (e.g. pre-training and data augmentation), and emphasise how to implement them in concordance with features of bioacoustic signals. We further discuss whether these networks can generalise the affective quality of vocalisations across different taxa.
2024
Artificial intelligence; machine learning; species discrimination; species recognition
01 Pubblicazione su rivista::01a Articolo in rivista
Bioacoustic classification of a small dataset of mammalian vocalisations using deep learning / Manriquez P, R.; Kotz, S. A.; Ravignani, A.; de Boer, B.. - In: BIOACOUSTICS. - ISSN 0952-4622. - 33:4(2024), pp. 354-371. [10.1080/09524622.2024.2354468]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1727159
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