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Cited by 24Music and Audio ProcessingAnimal Vocal Communication and BehaviorSpeech Recognition and Synthesis

Convolutional Neural Networks for the Identification of African Lions from Individual Vocalizations

Open access
Trapanotto, Martino, Nanni, Loris, Brahnam, Sheryl, Guo, Xiang·2022·Journal of Imaging
DOI 10.3390/jimaging8040096PMC PMC9029749
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Abstract

The classification of vocal individuality for passive acoustic monitoring (PAM) and census of animals is becoming an increasingly popular area of research. Nearly all studies in this field of inquiry have relied on classic audio representations and classifiers, such as Support Vector Machines (SVMs) trained on spectrograms or Mel-Frequency Cepstral Coefficients (MFCCs). In contrast, most current bioacoustic species classification exploits the power of deep learners and more cutting-edge audio representations. A significant reason for avoiding deep learning in vocal identity classification is the tiny sample size in the collections of labeled individual vocalizations. As is well known, deep learners require large datasets to avoid overfitting. One way to handle small datasets with deep learning methods is to use transfer learning. In this work, we evaluate the performance of three pretrained CNNs (VGG16, ResNet50, and AlexNet) on a small, publicly available lion roar dataset containing approximately 150 samples taken from five male lions. Each of these networks is retrained on eight representations of the samples: MFCCs, spectrogram, and Mel spectrogram, along with several new ones, such as VGGish and stockwell, and those based on the recently proposed LM spectrogram. The performance of these networks, both individually and in ensembles, is analyzed and corroborated using the Equal Error Rate and shown to surpass previous classification attempts on this dataset; the best single network achieved over 95% accuracy and the best ensembles over 98% accuracy. The contributions this study makes to the field of individual vocal classification include demonstrating that it is valuable and possible, with caution, to use transfer learning with single pretrained CNNs on the small datasets available for this problem domain. We also make a contribution to bioacoustics generally by offering a comparison of the performance of many state-of-the-art audio representations, including for the first time the LM spectrogram and stockwell representations. All source code for this study is available on GitHub.

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  • Gibbon duets, lion roars, and rook calls carry distinctive signatures that can be used to track individuals over time.

    “Gibbon duets, lion roars, and rook calls have all been shown to carry distinctive signatures that can be used to track individuals over time”

    From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Trapanotto et al. 2022

  • CNN- and MFCC-based models can now recognize individuals with 95-99% accuracy across taxa, from gibbons to lions, and multi-task networks can generalize across different call types.

    “CNN- and MFCC-based models can now recognize individuals with 95–99% accuracy across taxa, from gibbons to lions, and multi-task networks even manage to generalize across different call types”

    From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Trapanotto et al. 2022

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Research works printed but not linked: 5

  1. [3]Eklund R. Peters G.S. Ananthakrishnan G. Mabiza E. An acoustic analysis of lion roars. I: Data collection and spectrogram and waveform analyses Quarterly Progress and Status Report TMH-QPSR, Volume Fonetik 2011 Royal Institute of Technology Stockholm, Sweden 2011
  2. [23]Rabiner L.R. Schafer R.W. Theory and Application of Digital Speech Processing Prentice Hall Press Hoboken, NJ, USA 2010
  3. [54]O’Shaughnessy D.D. Speech Communication: Human and Machine Addison-Wesley Publishing Company Boston, MA, USA 1987
  4. [69]Tolkova I. Chu B. Hedman M. Kahl S. Klinck H. Parsing Birdsong with Deep Audio Embeddings arXiv 2021 2108.09203
  5. [72]Srivastava R.K. Greff K. Schmidhuber J. Highway Networks arXiv 2015 1505.00387

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Related

Authors
Trapanotto, M., Nanni, L., Brahnam, S., & Guo, X.
Affiliations
  • University of Padua (IT)
  • Missouri State University (US)
Corresponding author
Brahnam, Sheryl
ORCID
Venue
Journal of Imaging
Publisher
MDPI AG
Published
2022-04-01
Volume
8
Issue
4
Pages
96
ISSN
2313-433X (ISSN-L)
DOI
10.3390/jimaging8040096
PMC
PMC9029749
OpenAlex
W4224311674
Cited by
24
References
70
Citations by year
2026: 4 · 2025: 7 · 2024: 7 · 2023: 6
FWCI
2.31
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Imaging (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Padua Research Archive (University of Padova) (submittedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Developmental Biology › Animal Vocal Communication and Behavior
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Keywords
African lion, vocal individuality, lion roars, transfer learning, convolutional neural networks, small labeled datasets, sound representation, S-transform, VGGish, spectrogram, ensemble classifiers, passive acoustic monitoring, animal census

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