songsee

name	SongSee
skill_id	songsee
description	Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation.
version	1.0.0
source_kind	elephant-builtin
category	media
default_enabled	true

songsee

Generate spectrograms and multi-panel audio feature visualizations from audio files.

Prerequisites

Requires Go:

go install github.com/steipete/songsee/cmd/songsee@latest

Optional: ffmpeg for formats beyond WAV/MP3.

Quick Start

# Basic spectrogram
songsee track.mp3

# Save to specific file
songsee track.mp3 -o spectrogram.png

# Multi-panel visualization grid
songsee track.mp3 --viz spectrogram,mel,chroma,hpss,selfsim,loudness,tempogram,mfcc,flux

# Time slice (start at 12.5s, 8s duration)
songsee track.mp3 --start 12.5 --duration 8 -o slice.jpg

# From stdin
cat track.mp3 | songsee - --format png -o out.png

Visualization Types

Use --viz with comma-separated values:

Type	Description
spectrogram	Standard frequency spectrogram
mel	Mel-scaled spectrogram
chroma	Pitch class distribution
hpss	Harmonic/percussive separation
selfsim	Self-similarity matrix
loudness	Loudness over time
tempogram	Tempo estimation
mfcc	Mel-frequency cepstral coefficients
flux	Spectral flux (onset detection)

Multiple --viz types render as a grid in a single image.

Common Flags

Flag	Description
--viz	Visualization types (comma-separated)
--style	Color palette: classic, magma, inferno, viridis, gray
--width / --height	Output image dimensions
--window / --hop	FFT window and hop size
--min-freq / --max-freq	Frequency range filter
--start / --duration	Time slice of the audio
--format	Output format: jpg or png
-o	Output file path

Notes

• WAV and MP3 are decoded natively; other formats require ffmpeg
• Output images can be inspected with vision_analyze for automated audio analysis
• Useful for comparing audio outputs, debugging synthesis, or documenting audio processing pipelines