songsee

Generate spectrograms and feature panels from local MP3 or WAV files.

5.5k|641|Updated May 29, 2020

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

Ready to run

npx skills add https://github.com/the-open-agent/openagent --skill songsee-the-open-agent

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: songsee
Source: https://github.com/the-open-agent/openagent/tree/main/skills/songsee
Command: npx skills add https://github.com/the-open-agent/openagent --skill songsee-the-open-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the need for analyzing and visualizing audio recordings through spectrograms and feature visualizations, enabling quick insights into audio content.

Core Features & Use Cases

Spectrogram Generation: Create visual representations of audio frequency over time from tracks in MP3 or WAV formats.
Multi-Panel Visualization: Produce combined visualizations including mel, chroma, loudness, MFCC, and more for in-depth audio analysis.
Use Case: For audio researchers or musicians, generate detailed spectrograms and feature charts from a song to study its structure or troubleshoot audio quality.

Quick Start

Use the songsee tool to generate a spectrogram from an audio file named 'track.mp3'.

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 a local MP3 or WAV file?▼

To generate a spectrogram from a local MP3 or WAV file, you can use a tool that accepts command-line parameters to process audio signals and create visual representations of frequencies over time.

What audio features can I visualize for music analysis?▼

For music analysis, you can visualize audio features such as mel, chroma, loudness, and MFCC. These features are generated as multi-panel visualizations to provide in-depth insights into the audio signal.

Does audio visualization require ffmpeg for format decoding?▼

Yes, generating audio visualizations from local files requires ffmpeg for format decoding. This dependency ensures the tool can accurately read and process MP3 or WAV audio files for signal processing.

What is the best way to analyze audio structure and troubleshoot audio quality?▼

The best way to analyze audio structure and troubleshoot audio quality is by generating detailed spectrograms and feature charts from the audio track, enabling quick visual insights into the recording's frequency and loudness.

Can I customize visual styles when creating spectrograms for signal processing?▼

Yes, you can customize visual styles when creating spectrograms for signal processing. The tool accepts command-line parameters that allow you to tailor the visual representation to your specific research needs.

Why does my audio visualization tool fail to read local audio recordings?▼

Your audio visualization tool may fail to read local audio recordings if ffmpeg is not installed. This dependency is strictly required for decoding formats like MP3 and WAV before generating spectrograms.