Generate spectrograms and feature panels from audio files via CLI flags.
npx skills add https://github.com/joeleung0122/testing --skill songsee-joeleung0122
Please help me install this Agent Skill. Skill: songsee Source: https://github.com/joeleung0122/testing/tree/main/skills/songsee Command: npx skills add https://github.com/joeleung0122/testing --skill songsee-joeleung0122
Automates the creation of audio visualizations to replace manual spectrogram generation and feature extraction.
Run songsee on an audio file to generate spectrograms and feature panels.
High-intent search queries and answers about installing and using this skill.
To generate a spectrogram from an audio file, run the songsee binary via the command line on your track. It automatically processes the audio data and outputs visual spectrograms for quick analysis.
For music analysis, you can visualize mel, chroma, hpss, loudness, and tempo feature panels. These features are extracted and generated alongside the spectrogram to provide comprehensive audio content insights.
Yes, you can customize spectrogram dimensions, color palettes, and frequency ranges using configurable CLI flags. This allows you to tailor the visualization output to your specific analysis or presentation needs.
You need the songsee binary installed to run audio visualization tasks. The tool supports multiple decoding backends and requires no additional dependencies listed to process audio files and generate feature panels.
Audio visualization is suitable for podcast production, music research, and educational demonstrations. It helps you rapidly understand audio content by visually representing spectrograms and extracted features for clear analysis.
The best way to automate audio feature extraction and spectrogram generation is by using a CLI tool like songsee. It replaces manual generation by automatically processing tracks and outputting configurable visualizations.
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