/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