Generate spectrograms, mel, chroma, and MFCC visualizations from audio files.
npx skills add https://github.com/superfhp/lumi-agent --skill songsee-superfhp
Please help me install this Agent Skill. Skill: songsee Source: https://github.com/superfhp/lumi-agent/tree/main/skills/media/songsee Command: npx skills add https://github.com/superfhp/lumi-agent --skill songsee-superfhp
This Skill automates the generation of audio spectrograms and multi-panel visualizations, streamlining the analysis of audio files.
Generate a spectrogram for 'track.mp3' using the songsee skill.
High-intent search queries and answers about installing and using this skill.
To generate an audio spectrogram, you can use the songsee tool to automatically process audio files and create visual representations of frequency spectra over time.
For music analysis, you can visualize spectrograms, mel, chroma, and MFCC coefficients, rendering multiple audio feature visualizations in a single multi-panel image.
You need ffmpeg to extract features and visualize audio from various file formats, while Go and the songsee tool are required to execute the core generation workflow.
Yes, you can render multi-panel visualizations to display multiple audio features simultaneously, streamlining the analysis of audio files into a single comprehensive image.
The best way to automate audio analysis workflows for research is using songsee to generate multi-panel visualizations of spectrograms and MFCC coefficients from audio files.
huidgeGenerate multi-panel spectrogram visualizations from audio files.
AlvaroBianoGenerate spectrogram, mel, chroma, hpss, mfcc visualizations from audio files to PNG/JPG using songsee CLI.
dkyazzentwatwaAnalyze audio tempo, key, loudness, and frequency with waveform and spectrogram visualizations.
kevinnftGenerate spectrogram, Mel, and MFCC visualizations from audio files.
