songsee

To generate audio spectrograms, you install the songsee CLI and run it against an audio file path. It processes the input and outputs image files containing the requested spectrograms and feature visualizations.

For music analysis and signal processing, you can visualize standard spectrograms, mel spectrograms, chroma features, and MFCCs. These visualizations help you inspect audio content and debug signal processing pipelines across diverse datasets.

Yes, you need a Go toolchain installed to use this workflow. The CLI requires Go to build and run the commands that generate mel spectrograms, chroma features, and MFCC visualizations from your local audio files.

Yes, you can create multi-channel or grid visualizations for comparative analysis across tracks. This allows you to compare spectrograms and audio features side-by-side when researching datasets or debugging audio processing pipelines.

The best way to visualize chroma and mel features for audio research is using a CLI workflow that outputs multi-panel images. This approach provides quick visual insights into audio content without manual plotting scripts.

Audio spectrogram generation fails often due to incorrect input file paths or unsupported audio formats. The CLI includes basic error handling, but ensuring your Go toolchain is properly configured and file paths are valid is required.