Generate spectrograms and analyze mel, chroma, and MFCC audio features via CLI.
npx skills add https://github.com/aivos-xie/hermes-skills --skill songsee-aivos-xie
Please help me install this Agent Skill. Skill: songsee Source: https://github.com/aivos-xie/hermes-skills/tree/main/media/songsee Command: npx skills add https://github.com/aivos-xie/hermes-skills --skill songsee-aivos-xie
Analyze audio files and quickly generate visual representations of their features like mel, chroma, MFCCs via CLI, enabling rapid understanding and comparisons of audio signals.
Generate a spectrogram for the track.mp3 audio file with:
songsee track.mp3
High-intent search queries and answers about installing and using this skill.
You can generate a spectrogram by running the songsee CLI with your audio file. It processes the input and outputs visual representations of mel, chroma, and MFCC features for rapid signal analysis.
You need ffmpeg installed alongside the Go environment to analyze audio features from various formats. ffmpeg provides the necessary decoding support for processing additional audio formats.
Yes, you can compare multiple audio files side-by-side using the songsee CLI. It generates spectrograms and feature visualizations for each file, enabling quick comparisons of their audio characteristics.
The best way to visualize pitch and rhythm for debugging is using songsee to generate chroma and mel spectrograms. This provides immediate visual insights into the audio signal characteristics.
Yes, songsee requires a Go environment to run. The CLI is built using Go and depends on this environment to execute its audio analysis and feature generation processes.
MFCC analysis is used to represent the timbre of audio files. By generating MFCC visualizations, you can obtain insights on pitch, rhythm, and timbre to debug audio processing pipelines.
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