AI · AI model
FGC2.3: Feline Vocalization Classification and Cat Translation Project
Deep learning model and system classifying cat vocalizations into 40 categories
FGC2.3 is an audio classification system and deep learning model developed by Pattern of USA Inc. using CNN and LSTM architectures to classify feline sounds across 40 granular categories. Built on a dataset spanning food, life events, aggression, mating, and distress complaints, it achieves over 95% reported accuracy and powers a real-time cat vocalization translation app on iOS.
What it's for
- Translate cat meows and vocalizations into English phrases in real time
- Identify specific cat needs such as hunger, pain, discomfort, or mating behaviors
- Classify complex feline sound sequences and behavioral acoustic patterns for research
Features
- 40 distinct vocalization classes across 5 behavioral categories: Food, Life, Fight, Sex, and Complaint
- Support for single sounds, repeated sound sequences (RS), and complex sound sequences (CSS)
- Deep learning architecture combining CNN and LSTM networks
- Over 95% classification accuracy on curated audio datasets
- Deployment in an iOS application for real-time mobile audio translation
Pricing
Free. Research paper preprint available for free on ResearchGate
Details
- Type
- AI model
- Category
- AI
- Topic
- Audio Classification
- Open source
- No
- Platforms
- iOS, Web
- For
- Pet owners, animal behaviorists, and AI audio researchers
- Alternative to
- MeowTalk
- Tags
- audio, cats, translation, cnn, lstm, bioacoustics, ios
Why it was saved
- @niickjackson: Aims to help pet owners understand their cats' needs (such as hunger or attention) with a reported 95% accuracy rate.
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