songsee

To generate a spectrogram from an audio file, use the songsee CLI to process the track and output a visual representation of its frequencies over time. It supports time-slicing and frequency range filtering for detailed analysis.

For music analysis, you can visualize spectrograms, mel-frequency cepstral coefficients (MFCC), and chroma features. These feature panels help identify specific frequency patterns and loudness variations within an audio track.

Yes, you need ffmpeg to analyze non-native audio formats. The songsee binary processes native formats directly, but ffmpeg is required as an optional dependency to decode non-native audio files before generating visualizations.

Yes, you can customize the frequency range and output format of your spectrogram. The tool allows you to adjust visualization styles and output formats to suit specific audio engineering or research needs.

The best way to visualize loudness variations in a music track is by generating a multi-panel feature visualization. This approach automates the creation of spectrograms and audio feature panels to easily identify loudness changes.