Abstract
This work presents a dataset of cat vocalizations focusing on the meows emitted in three different contexts: brushing, isolation in an unfamiliar environment, and waiting for food. The dataset contains vocalizations produced by 21 cats belonging to two breeds, namely Maine Coon and European Shorthair. Sounds have been recorded using low-cost devices easily available on the marketplace, and the data acquired are representative of real-world cases both in terms of audio quality and acoustic conditions. The dataset is open-access, released under Creative Commons Attribution 4.0 International licence, and it can be retrieved from the Zenodo web repository.
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Notes
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The A-law algorithm is a standard used in European 8-bit PCM digital communications systems to optimize the dynamic range of an analog signal for digitizing.
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LPCM stands for Linear Pulse-Code Modulation, a standard method to digitally represent sampled analog signals. In a LPCM stream, the amplitude of the analog signal is sampled regularly at uniform intervals, and each sample is quantized to the nearest value within a range of digital linearly spaced steps.
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Please note that different envelope functions and frame sizes may have different optimal thresholds.
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References
Alías, F., Socoró, J.C., Sevillano, X.: A review of physical and perceptual feature extraction techniques for speech, music and environmental sounds. Appl. Sci. 6(5), 143 (2016)
Bradshaw, J., Cameron-Beaumont, C.: The signalling repertoire of the domestic cat and its undomesticated relatives. In: The Domestic Cat: The Biology of Its Behaviour, pp. 67–94 (2000)
Brown, S.L.: The social behaviour of neutered domestic cats (Felis catus). Ph.D. thesis, University of Southampton (1993)
Camacho, A., Harris, J.G.: A sawtooth waveform inspired pitch estimator for speech and music. J. Acoust. Soc. Am. 124(3), 1638–1652 (2008)
Cameron-Beaumont, C.: Visual and tactile communication in the domestic cat (Felis silvestris catus) and undomesticated small-felids. Ph.D. thesis, University of Southampton (1997)
Car Working Group: Headset Profile (HSP) 1.2. Bluetooth SIG (2008)
Carney, H., Gourkow, N.: Impact of stress and distress on cat behaviour and body language. In: Ellis, S., Sparkes, A. (eds.) The ISFM Guide to Feline Stress and Health. Tisbury (Wiltshire): International Society of Feline Medicine (ISFM) (2016)
Casey, R.: Fear and stress in companion animals. In: Horwitz, D., Mills, D., Heath, S. (eds.) BSAVA Manual of Canine and Feline Behavioural Medicine, pp. 144–153. British Small Animal Veterinary Association, Guarantee (2002)
Daniel, P., Weber, R.: Psychoacoustical roughness: implementation of an optimized model. Acta Acust. United Acust. 83(1), 113–123 (1997)
Dara, S., Tumma, P.: Feature extraction by using deep learning: a survey. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1795–1801 (2018)
Eriksson, M., Keeling, L.J., Rehn, T.: Cats and owners interact more with each other after a longer duration of separation. PLOS One 12(10), e0185599 (2017). https://doi.org/10.1371/journal.pone.0185599
Grey, J.M., Gordon, J.W.: Perceptual effects of spectral modifications on musical timbres. J. Acoust. Soc. Am. 63(5), 1493–1500 (1978)
Karsh, E.B., Turner, D.C.: The human-cat relationship. Domestic Cat: Biol. Behav. 159–177 (1988)
Lartillot, O., Toiviainen, P.: A matlab toolbox for musical feature extraction from audio. In: International Conference on Digital Audio Effects, pp. 237–244. Bordeaux (2007)
McComb, K., Taylor, A.M., Wilson, C., Charlton, B.D.: The cry embedded within the purr. Curr. Biol. 19(13), R507–R508 (2009)
Notari, L.: Stress in veterinary behavioural medicine. In: BSAVA Manual of Canine and Feline Behavioural Medicine, pp. 136–145. BSAVA Library (2009)
Ntalampiras, S., et al.: Automatic classification of cat vocalizations emitted in different contexts. Animals 9(8), 543.1–543.14 (2019). https://doi.org/10.3390/ani9080543
Owens, J.L., Olsen, M., Fontein, A., Kloth, C., Kershenbaum, A., Weller, S.: Visual classification of feral cat Felis silvestris catus vocalizations. Curr. Zool. 63(3), 331–339 (2017)
Palestrini, C.: Situational sensitivities. In: Horwitz, D., Mills, D., Heath, S. (eds.) BSAVA Manual of Canine and Feline Behavioural Medicine, pp. 169–181. British Small Animal Veterinary Association, Guarantee (2009)
Palestrini, C., et al.: Stress level evaluation in a dog during animal-assisted therapy in pediatric surgery. J. Veterinary Behav. 17, 44–49 (2017)
Plomp, R., Levelt, W.J.M.: Tonal consonance and critical bandwidth. J. Acoust. Soc. Am. 38(4), 548–560 (1965)
Pollard, H.F., Jansson, E.V.: A tristimulus method for the specification of musical timbre. Acta Acust. United Acust. 51(3), 162–171 (1982)
Pongrácz, P.: Modeling evolutionary changes in information transfer: effects of domestication on the vocal communication of dogs (Canis familiaris). Eur. Psychol. 22(4), 219–232 (2017)
Prato Previde, E., et al.: What’s in a meow? A study on human classification and interpretation of domestic cat vocalizations. Animals 10, 1–17 (2020). in press
Telephony Working Group: Hands-Free Profile (HFP) 1.7.1, Bluetooth Profile Specification. Bluetooth SIG (2015)
Vassilakis, P.: Auditory roughness estimation of complex spectra-roughness degrees and dissonance ratings of harmonic intervals revisited. J. Acoust. Soc. Am. 110(5), 2755 (2001)
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Ludovico, L.A., Ntalampiras, S., Presti, G., Cannas, S., Battini, M., Mattiello, S. (2021). CatMeows: A Publicly-Available Dataset of Cat Vocalizations. In: Lokoč, J., et al. MultiMedia Modeling. MMM 2021. Lecture Notes in Computer Science(), vol 12573. Springer, Cham. https://doi.org/10.1007/978-3-030-67835-7_20
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