PMOVES Cipher Beats Analyst

To group audio tracks by sound characteristics, you need to extract sonic fingerprints using loudness and spectral analysis. This Skill computes ebur128 loudness and aspectralstats via ffmpeg to cluster tracks into sonic constellations without relying on existing file metadata.

Generating M3U8 playlists from clustered audio data requires grouping tracks by their sonic fingerprints first. This Skill processes your audio archives to compute spectral statistics and automatically outputs M3U8 playlists for each identified sonic group for playback.

Yes, ffmpeg lavfi can extract loudness and spectral stats for audio fingerprinting by applying the ebur128 and aspectralstats filters. This Skill leverages ffprobe and ffmpeg lavfi to compute these robust sonic fingerprints directly from your input audio archives.

The best way to publish audio group geometry events to a NATS bus is to compute spectral clusters and map them to geometry events. This Skill uses Cipher memory with NATS integration to deterministically publish group geometry on the NATS Geometry Bus for CHIT visualization.

Yes, this audio analysis pipeline supports caching and re-clustering for large archives. It operates end-to-end across input datasets, utilizing deterministic analysis checkpoints and monitoring to efficiently manage and re-process your sonic fingerprint data.