Methodology

How hello&meow works

Every translation starts with acoustic science, not guesswork. We extract measurable features from your recording, classify them against established behavioral research, and use AI to generate a grounded interpretation.

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10
Acoustic features extracted
5
Behavioral categories
32+
Vocalization types identified
0
Audio ever leaves your device
Overview

Five behavioral categories, 32+ vocalization types

We classify vocalizations into five behavioral categories established in feline ethology research. Within each category, we identify the specific vocalization type based on acoustic signatures derived from peer-reviewed studies.

Feeding
Hunger, anticipation, food-related
Hunger Meow Anticipation Meow Food Solicitation Meow Pre-meal Repetitive Call Eating Sounds
Social
Contentment, bonding, greeting, play
Contentment Purr Solicitation Purr Greeting Trill Attention Meow Chirp / Chatter Play Vocalization Kneading Purr Bonding Trill
Defensive
Territorial, aggressive, warning
Territorial Growl Warning Hiss Aggressive Yell Spitting Defensive Growl Young Growl Young Hiss
Reproductive
Mating, estrus, courtship
Estrus Yowl Male Mating Call Courtship Trill Post-mating Vocalization
Distress
Pain, discomfort, urgency, health
Pain Cry Boredom Yowl Urgent Appeal Discomfort Meow Anxiety Call Cough Sneeze Panting / Wheeze
Context System

Context is the biggest accuracy lever

Research by Bradshaw (2013) demonstrated that identical acoustic patterns carry different meanings depending on situation — a meow at a food bowl vs. a meow at a closed door are the same sound but different communications. Our context chip system and cat profile make this distinction explicit.

Situation chips
Who is present, location, activity, and time of day are encoded as structured labels and injected directly into the AI prompt as situational context.
Cat profile
Name, breed, and age group affect interpretation. Siamese cats have a higher vocal baseline. Senior cats yowling at night warrant a different welfare assessment than a kitten doing the same.
Welfare flagging
Acoustic patterns associated with pain, illness, or distress are automatically escalated with a welfare note — regardless of confidence level on the primary classification.
Research foundation

Built on peer-reviewed ethology

hello&meow's behavioral categories, acoustic correlates, and interpretation framework are derived from the following publicly available research. The methodology is ours; the scientific foundation it stands on belongs to the field.

Schötz, S. (2006). "A phonetic pilot study of vocalizations in three cats." Proceedings Fonetik 2006, Lund University. — Foundational acoustic description of trill, meow, and call phonetics in domestic cats.
Nicastro, N. (2004). "Perceptual and acoustic evidence for species-level differences in meow vocalizations by domestic cats and African wild cats." Journal of Comparative Psychology, 118(3), 287–296. — Established that solicitation meows have rising F0 contours; humans perceive urgency from acoustic features.
McComb, K., Taylor, A.M., Wilson, C., & Charlton, B.D. (2009). "The cry embedded within the purr." Current Biology, 19(13), R507–R508. — Identified the solicitation purr: a high-frequency cry component embedded in a low-frequency purr that increases perceived urgency.
Bradshaw, J. (2013). Cat Sense: How the New Feline Science Can Make You a Better Friend to Your Pet. Basic Books. — Comprehensive behavioral framework for domestic cat communication; context-dependency of meow meaning.
de Souza Filho, A.G., et al. (2019). "Automatic classification of cat vocalizations in different contexts." PLOS ONE, 14(5), e0212284. — Machine learning classification of feline vocalizations; acoustic feature correlates by behavioral context.

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