Generate spectrograms and audio feature plots from audio files via CLI.
npx skills add https://github.com/ChangZhou-xj/zxj_skill --skill songsee-changzhou-xj
Please help me install this Agent Skill. Skill: songsee Source: https://github.com/ChangZhou-xj/zxj_skill/tree/main/media/songsee Command: npx skills add https://github.com/ChangZhou-xj/zxj_skill --skill songsee-changzhou-xj
Audio analysis is often manual and time-consuming; this Skill enables generating spectrograms and a suite of audio feature visualizations from audio files via a CLI, accelerating insight and documentation.
Run songsee on an audio file to produce a visualization image.
High-intent search queries and answers about installing and using this skill.
To generate a spectrogram from an audio file, run the CLI command which processes the audio data and outputs a visualization image for quick spectral analysis.
Besides a standard spectrogram, you can generate visualizations for mel, chroma, HPSS, self-similarity, loudness, tempogram, MFCC, and spectral flux to analyze various audio characteristics.
Yes, you can create multi-panel visualization grids to compare multiple audio features in a single image for debugging, comparison, or documentation purposes.
FFmpeg is optional for processing audio files; it provides support for additional audio formats, while the core feature extraction and visualization generation works without it.
The best way to visualize MFCCs for music analysis is using a CLI tool that generates dedicated MFCC plots from audio files, accelerating insight by automating the visualization process.
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