Timeline for How can you tell whether there's a signal (when your signal looks a lot like noise)?
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 19, 2012 at 2:57 | comment | added | Daniel R Hicks | Primarily just "spectral difference" (also referred to as "spectral flux") to detect "onset" and "offset". But my current scheme divides the spectrum into bands and "weights" each band based on the apparent signal strength in that band (which is judged by the amount of variation in spectral difference occurring at about 1/2 Hz). This works well to detect breathing/snoring across the entire volume range, even with significant noise, but it effectively loses all indication of actual volume. And simply looking at simultaneous total sound level doesn't work due to the noise problem. | |
| Feb 18, 2012 at 1:05 | comment | added | pichenettes | which features are you using for classification? | |
| Feb 18, 2012 at 0:19 | comment | added | Daniel R Hicks | I have in excess of 50 data sets. Unfortunately, an algorithm that works perfectly on one set fails miserably on the next. Some have TV going in the background, some have air handlers rumbling, etc. Breathing/snores can be barely audible or paint-peeling. A subject may change positions and entirely change the nature of his snoring. Some sort of self-tuning scheme is clearly required. | |
| Feb 17, 2012 at 19:17 | comment | added | pichenettes | If your training set is large enough, this might still be feasible using machine learning techniques. | |
| Feb 17, 2012 at 18:16 | comment | added | Daniel R Hicks | One problem is that the nature of the sound varies so much from individual to individual and even for a single individual within the course of a night. But thanks for the clues -- I'll look into them. | |
| Feb 17, 2012 at 8:36 | history | answered | pichenettes | CC BY-SA 3.0 |