u/WhopperWhopperWhop

Hey, for a class project at Georgia Tech, I made a tool that shows what audio features most contribute to the decision of a breath abnormality detection model trained on stethoscope and microphone recordings. I noticed that, as a result, it seems to effectively isolate wheezes from background sounds.

I wanted to see what approaches are usually taken for adaptively isolating inconsistently-pitched sounds of interest in noisy environments. I also wanted to see if anyone has any experience with any similar methods to determine what audible signatures an audio classification model is basing its decision off of.

I didn't get very technical in this demo, but if you have any questions, feedback, domain knowledge, or criticism, I would love to hear them to help prepare for my final report and presentation. Thanks!

https://youtu.be/0XP69l9UXzw

No Place I'd Rather Be jam