[stefanRfcx]

That's a great question. Actually, one of the sounds that are pretty close to a chainsaw are mosquitos that are circling around our microphones due to the Doppler effect. We found ways of dealing with signals that are close to chainsaws by aggregating multiple models and also a time-based analysis. The system can draw causal/correlative conclusions such as a vehicle is usually present before a chainsaw. If there's no vehicle, the likelihood of a chainsaw goes down and the chainsaw model must be highly confident before we sound an alert.

[tonic_section]

How do you quantify the confidence of your model? Do you use a Bayesian model or just the log-likelihood? Because the latter can act strangely in some cases.

[coolio2657]

I know this is a digression from the current discussion on how well the devices work, but as a stats student who just learned about estimating using log-likelihoods, could you give some more info on how that is inferior to the Bayesian model (since I've heard the exact opposite is true)?

[tonic_section]

The problem is that neural networks trained using maximum LL do not return calibrated probabilities, using e.g. the softmax output as 'confidence' of a model tends to result in overconfident predictions, take a look at adversarial attacks on neural networks for an extreme example: https://blog.openai.com/adversarial-example-research/

[ada1981]

Logger-likelyhood ;)