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/music-emotion — Emotion & Style Analysis
music-emotion
Analyze emotion — mood classification, energy, valence, genre detection
SKILL.md
Full skill instructions
/music-emotion — Emotion & Style Analysis
Classify the emotional content of an audio file: primary mood, energy level, emotional valence, arousal, genre, and mood tags.
Usage
/music-emotion <audio_file_path>
Steps
- Validate the audio file path
- Run emotion analysis:
python3 -m music_analyzer emotion "<audio_file_path>"
- Present results:
- Primary Emotion: Dominant mood (happy, sad, calm, energetic, etc.)
- Energy Level: 0-1 scale with curve across song segments
- Valence: -1 (negative) to 1 (positive)
- Genre: Detected genre
- Mood Tags: Descriptive mood keywords
Detection Methods
- CLAP (full tier): AI-based emotion/genre classification using CLAP model
- Heuristic (lite tier): Spectral features + rhythm + tonality-based rules
The method used is noted in the method field of the output.
Output Fields
| Field | Description |
|---|---|
primary_emotion | Dominant emotion label |
secondary_emotions | Additional emotion tags |
overall_energy | Energy level 0-1 |
energy_curve | Energy values per segment |
valence | Emotional valence -1 to 1 |
arousal | Arousal level 0-1 |
genre | Detected genre |
mood_tags | Mood descriptor keywords |
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