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songsee

Generate spectrograms and mel-frequency cepstral coefficients from audio files.

Updated Jun 17, 2026
Search Tags:#feature extraction#spectrogram#audio analysis#audio visualization#music analysis
One-click install
npx skills add https://github.com/Brilly-Bohyun/skill-repository --skill songsee-brilly-bohyun
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: songsee
Source: https://github.com/Brilly-Bohyun/skill-repository/tree/main/skills/media/songsee
Command: npx skills add https://github.com/Brilly-Bohyun/skill-repository --skill songsee-brilly-bohyun

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires songsee, and includes scripts (resource) and assets (resource) components.

What problem does it solve?

The songsee skill helps solve the need for generating visual representations of audio data such as spectrograms and other features for audio analysis.

Core Features & Use Cases

  • Audio Analysis: Provides spectrograms, mel-scaled spectrograms, and other audio feature visualizations.
  • CLI Interface: Easy to use through a command-line interface for a range of analysis tasks.
  • Versatile Outputs: Offers a variety of visualization types, including harmonic/pitched and percussive separation, and onset detection.
  • Use Case: This tool can be used for audio debugging, feature extraction in music applications, or audio engineering purposes.

Quick Start

Generate a spectrogram of the file 'track.mp3' using songsee.

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?▼

You can generate a spectrogram by running a command-line interface tool that processes audio files to extract visual representations like mel-scaled spectrograms and harmonic/percussive separations for music analysis.

What audio feature visualizations can I extract for signal processing workflows?▼

Available audio feature visualizations include spectrograms, mel-scaled spectrograms, mel-frequency cepstral coefficients, harmonic and percussive separation, and onset detection. These features support audio debugging and signal processing workflows.

Do I need Go and ffmpeg to extract audio features and visualize spectrograms?▼

Yes, you need the Go programming language installed to run the spectrogram generation tool. FFmpeg is optional but required for processing certain audio file formats during feature extraction and visualization.

Can I separate harmonic and percussive components in an audio track for analysis?▼

Yes, you can separate harmonic and pitched components from percussive elements in an audio track. This visualization type is available alongside onset detection to help analyze musical characteristics and audio engineering details.

What is the best way to visualize mel-frequency cepstral coefficients for audio debugging?▼

The best way to visualize mel-frequency cepstral coefficients for audio debugging is using a CLI-based music analysis tool. It generates these visual representations directly from audio files to assist with signal processing workflows.

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