Meow Decoder classifier heads
Small logistic-regression heads that turn a meow's audio embedding into the likeliest situation: waiting for food, enjoying attention, or stressed somewhere new. Used by meow-decoder, which downloads them automatically.
| File | Encoder | First guess right (unseen cats) | Top two | Calibration error | License |
|---|---|---|---|---|---|
beats.npz |
Earth Species Project esp_aves2_sl_beats_all |
65.9% | 89.1% | 2.2% | CC BY-NC-SA 4.0 (follows the encoder) |
ast.npz |
MIT AST fine-tuned on AudioSet | 61.8% | 84.1% | 4.7% | CC BY 4.0 (follows the CatMeows data) |
Always guessing the most common situation scores 50.2%. Scores are 5-fold cross-validation over whole cats
(splits.json in the code repo), so every test meow comes from a cat the head never saw.
Format
Plain NumPy arrays, no pickle: classes, mean, scale, coef, intercept (standardize, then multinomial
logistic regression), plus X, y (the 440 training embeddings, kept so the app can refit with your own cat's meows).
from meow_decoder import Decoder
Decoder("beats").predict("meow.wav")["results"]
Training data
CatMeows (Ludovico et al., 2020): 440 meows from 21 Maine Coon and European Shorthair cats, CC BY 4.0. Audio is 8 kHz, so nothing above 4 kHz was heard.
Limits
Three situations only. It cannot detect pain or illness. Pet cats from one dataset; other cats may sound different.