CatMeows: A Publicly-Available Dataset of Cat Vocalizations

Ludovico, Luca A.; Ntalampiras, Stavros; Presti, Giorgio; Cannas, Simona; Battini, M.; Mattiello, S.

doi:10.1007/978-3-030-67835-7_20

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…This can be observed in large datasets ([ 44 ] in terms of call categories), but it is even more common when the dataset size itself is moderate. It can then involve vocalization types, contexts or individuals [ 45 ] and even species in monitoring applications [ 43 , 46 ].…”

Section: A Sung Dataset: the Bonobo Recordingsmentioning

confidence: 99%

Improving the workflow to crack Small, Unbalanced, Noisy, but Genuine (SUNG) datasets in bioacoustics: The case of bonobo calls

et al. 2023

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Despite the accumulation of data and studies, deciphering animal vocal communication remains challenging. In most cases, researchers must deal with the sparse recordings composing Small, Unbalanced, Noisy, but Genuine (SUNG) datasets. SUNG datasets are characterized by a limited number of recordings, most often noisy, and unbalanced in number between the individuals or categories of vocalizations. SUNG datasets therefore offer a valuable but inevitably distorted vision of communication systems. Adopting the best practices in their analysis is essential to effectively extract the available information and draw reliable conclusions. Here we show that the most recent advances in machine learning applied to a SUNG dataset succeed in unraveling the complex vocal repertoire of the bonobo, and we propose a workflow that can be effective with other animal species. We implement acoustic parameterization in three feature spaces and run a Supervised Uniform Manifold Approximation and Projection (S-UMAP) to evaluate how call types and individual signatures cluster in the bonobo acoustic space. We then implement three classification algorithms (Support Vector Machine, xgboost, neural networks) and their combination to explore the structure and variability of bonobo calls, as well as the robustness of the individual signature they encode. We underscore how classification performance is affected by the feature set and identify the most informative features. In addition, we highlight the need to address data leakage in the evaluation of classification performance to avoid misleading interpretations. Our results lead to identifying several practical approaches that are generalizable to any other animal communication system. To improve the reliability and replicability of vocal communication studies with SUNG datasets, we thus recommend: i) comparing several acoustic parameterizations; ii) visualizing the dataset with supervised UMAP to examine the species acoustic space; iii) adopting Support Vector Machines as the baseline classification approach; iv) explicitly evaluating data leakage and possibly implementing a mitigation strategy.

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Section: A Sung Dataset: the Bonobo Recordingsmentioning

confidence: 99%

Improving the workflow to crack Small, Unbalanced, Noisy, but Genuine (SUNG) datasets in bioacoustics: The case of bonobo calls

et al. 2023

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Acoustic classification of individual cat vocalizations in evolving environments

Ntalampiras

Sánchez²

2021

2021 44th International Conference on Telecommunications and Signal Processing (TSP)

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This paper is focused on the classification of vocalizations characterizing intents of individual cats. Cats vocalize in order to convey different emotions and/or intents and although their repertoire/vocabulary may not be universal, it exhibits consistent characteristics on an individual basis. In this work, we present a complete pipeline for processing streams of audio, with the twofold goal being both detection as well as interpretation of cat vocalizations. The proposed system is based on YAMNet pre-trained deep network where we apply meaningful modifications addressing the requirements of the task-athand. Interestingly, the overall system is able to run in real-time on modern smartphones using the developed application. At the same time, we address the non-stationarity problem meaning that class dictionary is updated on-the-fly following user recommendations. After extensive experiments, we show that the proposed pipeline achieves quite satisfactory detection and recognition accuracy. To the best of our knowledge, this is the first attempt in the related literature to address continuous detection and interpretation of cat vocalizations in a real-time fashion.

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Investigation Of Infants’ Crying Detection In Noisy Home Scene With Deep Learning

Lin

Cheng

Lei

2022

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)

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CatMeows: A Publicly-Available Dataset of Cat Vocalizations

Cited by 5 publications

References 16 publications

Improving the workflow to crack Small, Unbalanced, Noisy, but Genuine (SUNG) datasets in bioacoustics: The case of bonobo calls

Improving the workflow to crack Small, Unbalanced, Noisy, but Genuine (SUNG) datasets in bioacoustics: The case of bonobo calls

Acoustic classification of individual cat vocalizations in evolving environments

Investigation Of Infants’ Crying Detection In Noisy Home Scene With Deep Learning

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