songsee

To generate an audio spectrogram, you provide an audio file like 'track.mp3' to create a detailed visual representation of its frequency content over time. This process helps identify specific sonic elements or issues within the track.

Yes, time slicing allows you to extract and visualize specific segments of an audio file for focused analysis. This targets exact regions of your audio data, generating detailed spectrograms and feature visualizations for just those selected moments.

Besides standard spectrograms, you can generate multi-feature visualizations such as Mel, Chroma, and MFCC panels. These feature visualizations help analyze complex audio characteristics for music information retrieval and signal processing tasks.

Yes, you need ffmpeg installed to process non-native audio formats for visualization. Native audio formats can be processed directly, but ffmpeg is required to decode and prepare other file types before generating spectrograms and feature panels.

Yes, you can customize FFT settings and frequency ranges when generating audio spectrograms. This allows you to adjust the visualization parameters to focus on specific frequency bands and tailor the analysis for your audio processing needs.