songsee

Name: songsee
Availability: InStock
Author: mark

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

31|3|Updated May 7, 2026

Search Tags:#visualization #audio #chroma #spectrogram #mfcc #mel

Ready to run

npx skills add https://github.com/markwang2658/hermes-windows-native --skill songsee-markwang2658

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: songsee
Source: https://github.com/markwang2658/hermes-windows-native/tree/main/hermes-agent/skills/media/songsee
Command: npx skills add https://github.com/markwang2658/hermes-windows-native --skill songsee-markwang2658

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Generate visual representations of audio, including spectrograms and MFCCs, from raw audio files to speed up analysis and sharing.

Core Features & Use Cases

Generate spectrograms, Mel, chroma, and MFCC visualizations from audio files.
Create multi-panel visualizations to compare features across tracks.
Use cases include music analysis, audio research, and debugging workflows that need quick, shareable visuals.

Quick Start

Run songsee on an input audio file to generate a spectrogram and feature visualizations.

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 for music analysis?▼

To generate a spectrogram from an audio file, you can use a CLI tool that processes raw audio to create visual frequency representations. This speeds up music analysis by providing quick visual insight into frequency content and audio features.

Can I visualize MFCC and chroma features from audio tracks?▼

Yes, you can visualize MFCC and chroma features from audio tracks. The tool generates multi-panel visualizations to compare these features across tracks, which is highly applicable for audio research and debugging workflows.

Do I need Go and ffmpeg to run audio spectrogram generation tools?▼

You need Go to build and run the CLI for audio spectrogram generation, while ffmpeg support is optional. Ffmpeg provides additional format compatibility if your raw audio files require decoding beyond standard formats.

What is the best way to compare frequency content across multiple audio files?▼

The best way to compare frequency content across multiple audio files is by creating multi-panel audio feature visualizations. This approach places spectrograms and Mel features side-by-side to speed up analysis and sharing.

Why use Mel spectrograms for audio debugging workflows?▼

Mel spectrograms are used for audio debugging workflows because they visually represent frequency content on a perceptually relevant scale. This helps quickly identify anomalies and compare features across tracks during research.