songsee

You can generate an audio spectrogram from an MP3 file by running a CLI tool that processes input audio and renders visual representations. Optional ffmpeg is required for decoding formats beyond WAV and MP3.

Audio features available for music analysis visualization include mel, chroma, MFCC, loudness, tempogram, and spectral flux. These derived feature types can be rendered together as a single multi-panel image for side-by-side comparison.

You need ffmpeg to extract audio features and create spectrograms only when decoding formats beyond standard WAV and MP3 files. The core processing handles WAV and MP3 natively without requiring additional dependencies.

You can visualize a specific time slice of a track instead of the full audio by specifying a start time and duration parameter. This produces focused feature visualizations for analyzing particular regions like a chorus or brief audio event.

The best way to compare melody and onset behavior in audio tracks is by rendering multiple visualization types together as a single multi-panel image. This approach produces side-by-side diagnostic views for direct inspection of different audio feature representations.

songsee requires the songsee CLI installed via Go to produce audio feature images. It has no mandatory external dependencies, though optional ffmpeg is needed for decoding audio formats beyond WAV and MP3.