My algorithm correctly detects the time skew of two waveforms. Here’s the raw data from two mics:<\/p>\n\n\n\n And here’s after the skew is corrected for:<\/p>\n\n\n\n The two waveforms are a very good match.<\/p>\n\n\n\n The Bad<\/strong><\/p>\n\n\n\n The algorithm is very computationally intensive. My first pass at the code, finding the skew took 10-20 minutes for two 50 millisecond waveforms. With a little optimization from caching interpolation results and discarding excess precision, I got it down to 1-2 minutes (much better, but still pretty slow. I may be able to get another factor of 2 or 3 my switching to C++ from Python, but getting the code right will be more difficult.<\/p>\n\n\n\n The Skewed<\/strong><\/p>\n\n\n\n It has occasionally detected skews in the range I’d expect for two microphones next to each other (a few hundred microseconds), but most of the skews have been in the 2-2.5 millisecond range, which is about 10 times what I’m hoping for. More work on time sync is needed apparently.<\/p>\n","protected":false},"excerpt":{"rendered":" One of the technical challenges in this project is to figure out the exact time offset of two waveforms. I think I’ve solved that sufficiently. The Good My algorithm correctly detects the time skew of two waveforms. Here’s the raw data from two mics: And here’s after the skew is corrected for: The two waveforms… Continue reading The good, the bad, and the skewed<\/span> ![\"\"](\"https:\/\/boom-tracker.com\/tracker\/wp-content\/uploads\/2020\/07\/mic_001_004_1595605859-1024x548.png\")
![\"\"](\"https:\/\/boom-tracker.com\/tracker\/wp-content\/uploads\/2020\/07\/mic_001_004_1595605859_skewed-1024x548.png\")