Watches are precise machinery that require maintenance and well known watch brands take great pride in the longevity of their pieces, meaning they provide support for the lifetime of the watch (you will pay for this though). Typically, every 5-10 years you send them in for a service. Overtime, the little pieces and minuscule drops of lubricant wear and age and need replaced. Visibility into these metrics can help you keep an eye out for problems.
Higher end watches also get external certifications that reflect different precision standards. Some examples: METAS, COSC, or Rolex Superlative Chronometer if you are Rolex and need to be special. They have different specs, Superlative Chronometer is +/-2 sec per day. If it's out of spec and you're under warranty, you may be entitled to a free adjustment by a service center. Otherwise, overtime, as the performance degrades, it's a signal you may need a service.
There's also the risk of magnetization. If the delicate machinery becomes magnetized, you'll see BIG swings, like +/- minutes per day. Demagnitization is something any watchmaker can do quickly. (There is inherently some risk posed by the phone itself having a lot of magnets, but modern watches are typically built to resist magnetization to varying degrees -- look at the Rolex Milgauss as an example of best-effort magnet resistance)
Watches will also perform variably depending on position. If you know your watch is -4sec/day on your wrist, but +6sec/day face down, you can effectively manage it's accuracy by placing it face down over night and never have to unscrew the crown but keep a true time. This is a very common use case.
Hope that covers the general cases. This app avoids a lot of even deeper complexity, like beat error and amplitude which are deeper metrics describing the movements performance and guide watchmakers to know which screws to adjust which way.
This is the way. I got one as well to validate my results. Cost is the difference. If you're okay with close-enough and saving $145, I think the apps fit in a nice place.
I will say, this flow is not the primary concept for the app. My intended design, and what it's been for 2+ years, is a tool that provides structured flows for inputting the time on your device and comparing it to atomic time (provided in my app). If you stagger these measurements over 6 or so hours for a day or two, you get a very precise reading and I still recommend this approach as being better.
This is designed to be used by the internal device microphone. And this is in my write up, but if you have Airpods in all the time (like me), it will try to use that microphone and it will not work, and it will cause you to lose a day in frustration if you are working on something similar
I have this in open beta on Android right now so you're welcome to give it a shot and let me know :)
One difference is cost. I know that mine is lower cost, and is a one time unlock -- no subscription. So there's that. I'm also therefore positioned a little bit more towards the casual user, I prioritize speed of completion which means I need high confidence in a good signal quickly and don't hesitate to cancel mid-flow.
I think one thing alternatives offer is the ability to run for longer -- like a 10 minute check for instance. There are marginal gains in precision for every additional second, but they seem to diminish pretty quickly past 30 secs or so. But that's one difference I'm aware of.
This is definitely the way. Even the old wired mics Apple phones used to ship with that had the little rectangle mic works better because you can press it right up against the case back.
I'll do some research on this! I have a nagging feeling that there is more I can do to get the most out of this, but struggle with knowing where to look since this is not my background. I'm learning a lot as I go!
I built an audio timegrapher feature for my watch accuracy app, ChronoLog. Professional timegraphers use a piezo contact sensor and can cost upwards of $1,000. I wanted to do it with a phone mic.
The problem: an iPhone's built-in microphone picks up a mechanical watch's tick at about 1.5 dB SNR. The solution turned out to be epoch folding — the same technique radio astronomers use to find pulsars. Stack 100+ tick periods together and you get +20 dB of effective gain, enough to reliably measure rate and beat error.
The post covers the full DSP pipeline — bandpass filtering, epoch folding, autocorrelation (and why it finds harmonics before fundamentals at low SNR), Kalman filtering for convergence — and what I learned from five rounds of device testing.