Archive enrichment
Drop 50 hours of unstructured recordings, get back tags, speaker turns, silence regions, and a searchable schema.
Tags, QA flags, and search for audio at scale.
Less about creativity, more about structure. SignalLab turns piles of audio files into searchable, taggable, QA-checked assets that downstream tools can actually use.
Drop in audio, get back technical tags, semantic hints, risk flags, and a metadata schema your stack can ingest.
Files are decoded in your browser. JSON output is download-only.
How this is measuredDrop 50 hours of unstructured recordings, get back tags, speaker turns, silence regions, and a searchable schema.
Block files at upload time when they fail silence, clipping, or noise-floor thresholds.
Find every clip with "outdoor crowd noise + male speech" across a content library without listening to anything twice.
SignalLab treats audio like data. Schema-first, API-first, and built so the people downstream — editors, ML teams, archive managers — can actually use the output.
Also in SignalLab
Catch the bad parts before your editor does.
Auto-mark turns, silences, and scene changes.
Continue the workflow
Send the full file to MixLab for BS.1770-4 LUFS analysis, true-peak, and tonal balance on the program feed.
When SignalLab finds voice content, push the same audio to VoiceLab for sibilance and noise-floor scoring.
Region timestamps from SignalLab make it explicit which sections need accessibility attention in HearLab.
Indexed regions translate naturally into broadcast cue triggers — plan it in CueLab.
SkillLab includes drills on voice-vs-music-vs-noise classification, the same task SignalLab automates.
Each lab can hand audio off to the others without re-uploading. When you finish an analysis here, the → Lab buttons in the demo chrome carry your audio directly into the next lab’s analysis flow.