Pet Vocal Emotion Analysis

Problem

Pet vocalizations are often reduced to a count, but the emotional and behavioral meaning behind barking, whining, or growling is not directly visible. This skill targets developers and users who have cat or dog vocal audio/video and want a readable structured interpretation instead of a single coarse label.

How It Works

• Input: supports local mp3, wav, mp4 files or network URL; remote resources are handled by the API service.
• Acoustic analysis: extracts frequency-domain features, energy distribution, and temporal envelope to distinguish vocalization types.
• Emotion and intention inference: outputs states such as happy, excited, anxious, painful, alert, and intentions such as seeking attention, hunger, or needing to go out.
• Reports and history: generates structured analysis reports and can read historical report lists from the cloud interface via --list.
• Integration: structured fields can feed downstream reminders, rule checks, or content display.

Boundaries and Notes

Accuracy depends on background noise, recording clarity, and segment completeness; provide a clear vocalization clip of 3-30 seconds. The output is best used as interaction reference, not medical diagnosis. Historical reports must be fetched from the cloud interface, not local memory or manual summaries.