What audio formats does Songsee support?

It natively decodes WAV and MP3 files, and can handle almost any other format if FFmpeg is installed on your system.

What types of audio features can I visualize?

Beyond standard spectrograms, you can visualize mel-spectrograms, chroma, hpss, self-similarity, loudness, tempograms, MFCC, and flux.

Can I visualize specific parts of a long audio file?

Yes, you can use the --start and --duration flags to pinpoint and visualize specific time slices of an audio track.

Is it possible to customize the look of the generated charts?

Absolutely. You can choose from several color palettes including classic, magma, inferno, viridis, and gray using the --style flag.

Audio Visualizer & Spectrogram Generator

Name: Audio Visualizer & Spectrogram Generator
Author: HarleyCoops

byHarleyCoops

데이터 과학 및 ML

Generates high-quality spectrograms and comprehensive audio feature visualizations directly from the command line.

소개

The Songsee skill integrates advanced audio analysis capabilities into Claude Code, allowing users to transform audio files into detailed visual representations. It supports generating basic spectrograms as well as complex multi-panel grids covering Mel scales, chromagrams, loudness, and MFCCs. This tool is invaluable for developers, researchers, and audio engineers who need to perform rapid signal processing analysis or create visual documentation of audio data characteristics without leaving the terminal.

주요 기능

Supports native WAV/MP3 decoding with FFmpeg fallback for extensive format support
Extract specific audio metrics including chroma, HPSS, self-similarity, and tempograms
Perform precision time-slicing to analyze specific segments of audio tracks
0 GitHub stars
Generate high-resolution spectrograms and multi-panel feature visualizations
Customize visual aesthetics with built-in styles like Magma, Inferno, and Viridis

사용 사례

Visualizing frequency distribution and harmonics for music production and signal analysis
Automating the creation of audio data visualizations for machine learning datasets
Debugging audio processing algorithms by inspecting feature extractions like MFCCs and flux

소개

주요 기능

Supports native WAV/MP3 decoding with FFmpeg fallback for extensive format support
Extract specific audio metrics including chroma, HPSS, self-similarity, and tempograms
Perform precision time-slicing to analyze specific segments of audio tracks
0 GitHub stars
Generate high-resolution spectrograms and multi-panel feature visualizations
Customize visual aesthetics with built-in styles like Magma, Inferno, and Viridis

사용 사례

Visualizing frequency distribution and harmonics for music production and signal analysis
Automating the creation of audio data visualizations for machine learning datasets
Debugging audio processing algorithms by inspecting feature extractions like MFCCs and flux