songsee

Name: songsee
Availability: InStock
Author: davpatel605-beep

Generate spectrograms and feature plots from audio inputs.

Updated Jun 25, 2026

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

Ready to run

npx skills add https://github.com/davpatel605-beep/hermusagent --skill songsee-davpatel605-beep

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: songsee
Source: https://github.com/davpatel605-beep/hermusagent/tree/main/backend/vendor/hermes/skills/media/songsee
Command: npx skills add https://github.com/davpatel605-beep/hermusagent --skill songsee-davpatel605-beep

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps users understand and inspect audio files by transforming raw audio into visual representations of frequency, pitch, rhythm, and signal characteristics.

Core Features & Use Cases

Audio Visualization: Generates spectrograms and multi-panel feature visualizations including mel spectrograms, chroma, MFCC, loudness, and tempo analysis.
Audio Analysis Workflows: Supports comparing audio outputs, debugging synthesis pipelines, and documenting music or signal processing experiments.
Use Case: Analyze a music track to inspect its frequency content, harmonic structure, and rhythmic features through generated visual reports.

Quick Start

Use the songsee skill to generate a spectrogram visualization from the provided audio file.

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 for music analysis, the Skill transforms raw audio signals into visual representations of frequency and pitch. It processes supported audio inputs to create detailed visual reports for inspecting harmonic structure and rhythmic features.

What audio features can I visualize when debugging a signal processing pipeline?▼

When debugging a signal processing pipeline, you can visualize mel spectrograms, chroma, MFCC, loudness, and tempo. These multi-panel feature plots help inspect complex audio characteristics and evaluate synthesis outputs.

Do I need any specific CLI tools installed to visualize audio spectrums?▼

Yes, visualizing audio spectrums requires the songsee CLI functionality to operate. You need this command-line interface to process supported audio inputs and generate spectrograms with configurable analysis parameters.

Can I use this audio visualization approach to compare multiple audio outputs?▼

Yes, you can use audio visualization to compare multiple audio outputs by generating visual reports for each file. Comparing spectrograms, MFCC, and loudness plots helps identify differences in frequency content and signal characteristics.

What is the best way to inspect frequency content and rhythmic features in a music track?▼

The best way to inspect frequency content and rhythmic features in a music track is by generating a multi-panel visualization. This approach transforms raw audio into visual representations of frequency, pitch, and rhythm for detailed analysis.