songsee

To generate a spectrogram and visualize audio features like MFCCs, use this skill to create a multi-panel image grid containing mel, chroma, and MFCC data from your audio file.

Yes, you can use ffmpeg to visualize non-native audio formats. This skill optionally requires ffmpeg to decode audio formats before generating spectrograms and multi-panel feature visualizations.

Yes, you need the Go binary installed to extract chroma and mel-scaled data. The songsee Go binary is a required dependency for processing audio files and generating feature visualizations.

The best way to debug audio synthesis outputs and compare spectral signatures is to generate multi-panel visualizations containing spectrograms and harmonic data to inspect spectral characteristics for quality control.

To perform harmonic and percussive separation for music production analysis, use this skill to process your audio file and generate visualizations that estimate tempo and separate audio components.

Limitations include dependency on the Go binary for core processing and optional ffmpeg for non-native audio format decoding, meaning unsupported input formats may fail without proper ffmpeg integration.