Generate spectrograms and audio feature charts from audio files via command line.
npx skills add https://github.com/paxlabs-inc/ion-agent --skill songsee-paxlabs-inc
Please help me install this Agent Skill. Skill: songsee Source: https://github.com/paxlabs-inc/ion-agent/tree/main/skills/media/songsee Command: npx skills add https://github.com/paxlabs-inc/ion-agent --skill songsee-paxlabs-inc
This skill addresses the difficulty of manually analyzing audio files by providing a command-line interface to generate visual representations of sound, such as spectrograms and pitch distributions.
Use the songsee skill to generate a multi-panel visualization grid for the audio file track.mp3 by running the command songsee track.mp3 --viz spectrogram,mel,chroma,hpss,selfsim,loudness,tempogram,mfcc,flux.
High-intent search queries and answers about installing and using this skill.
To generate an audio spectrogram and MFCC charts, use the command-line tool to process your audio file and output a multi-panel visualization grid. Run a command like songsee track.mp3 --viz spectrogram,mfcc to create the visual representations.
Yes, the audio visualization CLI requires ffmpeg to decode non-native audio formats like mp3. The songsee binary handles native formats and analysis, but ffmpeg is an optional dependency needed for broader audio file format support.
Yes, you can visualize pitch class distribution and harmonic separation by requesting chroma and hpss visualizations. These audio analysis charts help debug music synthesis by verifying timing and onset detection accuracy in your audio output.
The best way to compare audio outputs visually is to generate multi-panel visualization grids including mel-spectrograms, spectral flux, and loudness charts. This provides visual evidence to document processing pipelines and compare different audio outputs.
Besides a standard spectrogram, you can visualize mel-spectrograms, chroma, MFCCs, harmonic-percussive source separation, self-similarity, loudness, tempograms, and spectral flux in a single complex grid image.
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