Generate spectrograms and feature-panel visualizations from audio files.
npx skills add https://github.com/Arry8/openclaw-edge --skill songsee-arry8
Please help me install this Agent Skill. Skill: songsee Source: https://github.com/Arry8/openclaw-edge/tree/main/skills/songsee Command: npx skills add https://github.com/Arry8/openclaw-edge --skill songsee-arry8
Generate visual representations of audio data to simplify analysis, comparison, and presentation.
Run songsee track.mp3 to generate a spectrogram and feature panels.
High-intent search queries and answers about installing and using this skill.
You can generate spectrograms and feature-panel visualizations from MP3 or WAV files by running the songsee CLI. It natively decodes WAV and MP3 formats to create visual representations for audio analysis workflows.
Yes, FFmpeg is optionally used to decode audio formats beyond native WAV and MP3 support. This allows spectrogram generation and feature-panel visualization across a wider range of audio inputs.
Batch process multiple audio tracks for music analysis by utilizing the songsee command-line workflow. It supports pipelines of many tracks, enabling automated spectrogram and feature-panel generation across large audio datasets.
Besides the spectrogram, songsee visualizes audio features like tempo, chroma, and MFCC. These feature panels provide detailed music analysis data to support research and educational demonstrations alongside standard spectrograms.
A spectrogram visually represents the frequency spectrum of audio data over time. In audio analysis, it simplifies the comparison and presentation of audio characteristics, aiding music research and educational demonstrations.
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