I have a project where I am detecting error states of a fan. Normal, zip tie in the fan, banging with a hammer at periodic intervals, and banging with a small screwdriver at periodic intervals to begin with.
I am running audio inference on a 1 second window at a time over a 10 second recording. The point of this is that new error states may present themselves temporally differently and the DSP should be suitable for that possibility. (some error states may be periodic bangs, others may be consistent change in pitch, etc.) Basically, I am trying to cast the net wide because I don’t know what errors will occur or how they will present themselves.
The idea is to have it automatically detect new possible error states and notify me so that I can retrain on various conditions (some potentially being new classifications), using the anomaly detection.
Since the window size is a little large, I have used the MFE DSP with default settings but a FFT length of 512 to pick up more discreet changes in the pitch or fast bangs over the second. This seems to work surprisingly well with the spectrogram and with the NN. The issue is with the anomaly detection.
The output is comprised of a few thousand “audio features”. When I select all features, the bubbles created exceed the size of the plotted points by a huge margin. I am only seeing the “cluster count” and “minimum score” settings, neither of which fix the issue. I have tried with different DSP settings to no avail. It is difficult to determine what the issues may even be here or how to address them, and I haven’t seen many forum posts or much documentation on this. Any info or suggestions would be much appreciated!