songsee

Generate spectrograms and audio feature analyses from audio files via command line.

Updated Jun 25, 2026

Search Tags:#analysis #visualization #signal processing #audio #music #spectrogram

Ready to run

npx skills add https://github.com/Rheasilvia/hermes-desktop --skill songsee-rheasilvia

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: songsee
Source: https://github.com/Rheasilvia/hermes-desktop/tree/main/skills/media/songsee
Command: npx skills add https://github.com/Rheasilvia/hermes-desktop --skill songsee-rheasilvia

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires go, ffmpeg.

What problem does it solve?

This Skill addresses the difficulty of manually analyzing audio files by providing a streamlined way to generate visual representations of sound data.

Core Features & Use Cases

Multi-panel Visualization: Generate grids containing spectrograms, mel-frequency cepstral coefficients, and pitch class distributions.
Audio Analysis: Perform harmonic/percussive separation and tempo estimation for music production or research.
Use Case: A sound engineer can use this to compare the spectral characteristics of two different audio masters to identify frequency imbalances or clipping issues.

Quick Start

Use the songsee skill to generate a multi-panel visualization grid for the audio file track.mp3 including spectrogram, mel, and chroma features.

Frequently Asked Questions about songsee

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate a spectrogram from an audio file?▼

Generate a spectrogram from an audio file by using command-line processing to extract and visualize frequency data over time. This skill outputs multi-panel grids containing spectrograms, mel-frequency cepstral coefficients, and chroma features.

What audio features can I visualize for music information retrieval?▼

Visualize audio features for music information retrieval including spectrograms, MFCC, tempogram, pitch class distributions, and harmonic/percussive separation. These features support frequency analysis, tempo estimation, and music production research tasks.

Do I need ffmpeg to process diverse audio formats for visualization?▼

You need ffmpeg to process diverse audio formats for visualization and comprehensive audio decoding. While the core processing requires Go, ffmpeg is an optional dependency specifically needed to support format flexibility beyond standard inputs.

Can I compare the spectral characteristics of different audio masters?▼

Compare the spectral characteristics of different audio masters by generating multi-panel visualization grids for each track. This allows sound engineers to visually identify frequency imbalances, clipping issues, and tonal differences across masters.

What is the best way to perform harmonic and percussive separation on a track?▼

Perform harmonic and percussive separation on a track by applying command-line audio analysis to split the input into its tonal and rhythmic components. This process yields distinct visual representations for music production analysis.

Does this audio analysis approach work without installing Go?▼

This audio analysis approach does not work without installing Go, as Go is a required dependency for the command-line processing logic. Optional ffmpeg is additionally needed for comprehensive audio decoding and diverse format support.