/docs
Documentation: the API, MCP, and every lab.
Written like a working engineer wrote them, because one did: quickstarts for the hosted analysis API and the MCP server, plus practical references on DSP, Android realtime audio, accessibility patterns, voice QA, and the engineering choices behind each lab.
API quickstart
First call in under two minutes: loudness, true peak, voice QA as JSON.
Connect an AI (MCP)
Copy-paste config for Claude, ChatGPT, Cursor and your own agents.
Guides & insights
Loudness targets, true peak, EBU R128 and more, written people-first.
Building with an LLM? Every page is also plain markdown: /api.md · /llms.txt
01 MixLab
- IntroWhat integrated LUFS actually tells youIntegrated LUFS is the loudness number you should care about, and the one most "loudness" tools get subtly wrong.
- PracticalReading crest factor without fooling yourselfCrest factor is the easiest dynamics metric to compute and the easiest to misread.
- DeepMid/Side decomposition in WebAudioSum and difference signals are the right way to think about stereo. Here is the WebAudio API code to do it.
02 VoiceLab
- IntroWhy filler density matters more than filler countCounting "ums" is the wrong measurement. Density tells you whether they are a problem.
- PracticalA pragmatic loudness target for podcastsThe right loudness target for spoken-word podcasts is not the same as music. Here are the numbers and why.
- DeepEstimating room echo without RT60You don’t need an impulse response to know your room is too live. Here are three pragmatic measurements.
03 HearLab
- PracticalLive Caption on Android: what it does and doesn't doGoogle’s Live Caption is the most-used accessibility feature in the world. Here is how it actually works and what it doesn’t cover.
- DeepHearing-aid routing through Android Audio FrameworkHow AAudio, ASHA, and LE Audio actually deliver audio to hearing devices on modern Android.
04 SignalLab
- IntroA useful tag schema for audio archivesWhat to put in your audio metadata so downstream tools can actually use it.
- PracticalStreaming clipping detection at scaleHow to detect clipping in audio at ingest time without decoding the whole file.
- DeepSpeaker turn segmentation: the pragmatic stackDiarisation is a hard ML problem. Speaker turn segmentation, the cheap version, is solved enough to use everywhere.
05 CueLab
- IntroA reusable pre-show checklistThe checklist that survives the moment when your mind goes blank thirty seconds before going live.
- PracticalOBS audio routing without surprisesOBS audio routing is more powerful than the default UI suggests. Here is the mental model that makes it predictable.
- DeepDesigning cue-list data that survives a re-orgA cue list is a data model first and a UI second. Treat it like one.
06 SkillLab
- IntroDesigning tiered audio challengesBronze / Silver / Gold tiers exist for a reason. Here is the structure that produces real skill gain.
- PracticalFeedback that lands: patterns from real coachesGeneric feedback is worse than no feedback. Here are the patterns that real coaches use.
- DeepProgression loops without slot-machine mechanicsHow to keep people coming back without weaponising dopamine.