mlsanityData to 14 Oct 2026

Feature Representations for Automatic Meerkat Vocalization Classification

arXiv 2408.15296 ↗First posted 27 Aug 2024eess.AS cs.LG cs.SDcited by 3

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Abstract (arXiv)

Understanding evolution of vocal communication in social animals is an important research problem. In that context, beyond humans, there is an interest in analyzing vocalizations of other social animals such as, meerkats, marmosets, apes. While existing approaches address vocalizations of certain species, a reliable method tailored for meerkat calls is lacking. To that extent, this paper investigates feature representations for automatic meerkat vocalization analysis. Both traditional signal processing-based representations and data-driven representations facilitated by advances in deep learning are explored. Call type classification studies conducted on two data sets reveal that feature extraction methods developed for human speech processing can be effectively employed for automatic meerkat call analysis.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 7 of its 25 references that are in the map. First posted on arXiv 27 Aug 2024.

Builds on

7 of 25 references.

References · 7

  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  2. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
  3. 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
  4. 2021SUPERB: Speech processing Universal PERformance Benchmarkcited by 666
  5. 2019catch22: CAnonical Time-series CHaracteristicscited by 105
  6. 2013Highly comparative time-series analysis: The empirical structure of time series and their methodscited by 30
  7. 2023Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?cited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processingcited by 9
  2. 2025Towards Leveraging Sequential Structure in Animal Vocalizationscited by 1
  3. 2026Beyond the Baseband: Adaptive Multi-Band Encoding for Full-Spectrum Bioacoustics Classificationcited by 0