In the few short years since we did our study, neural networks have really emerged as being a dominant approach to signal classification, and they've shown a much better ability to reach almost human-like performance in their ability to distinguish one sound from another.

So what we actually do is we transform the sound into an image format.

We take the sound and turn it into a spectrogram, which has the time on the X-axis, the frequency of the signal on the Y-axis, and then you also have a third dimension or the amplitude of each little coordinate in this X-Y graph, which tells you how loud that specific time frequency was.

And so by converting our signals into spectrograms, we're able to use neural networks frameworks that are very efficient for image processing, and they've been really well-suited for this task because you can't be sending your signals up to the cloud all the time.

It's just too power-intensive, right? So you need to have a detector that's both accurate and also lightweight enough to run in real time.

It also had the benefit of being relatively small.

Every neural network has a parameter count, which is... basically you can think of it as, like, the amount of knobs that you're turning to tune this model in order to better classify whatever you're classifying.

And some models, like ChatGPT, has over, like, many billions of parameters.

In the few short years since we did our study, neural networks have really emerged as being a dominant approach to signal classification, and they've shown a much better ability to reach almost human-like performance in their ability to distinguish one sound from another.

So what we actually do is we transform the sound into an image format.

We take the sound and turn it into a spectrogram, which has the time on the X-axis, the frequency of the signal on the Y-axis, and then you also have a third dimension or the amplitude of each little coordinate in this X-Y graph, which tells you how loud that specific time frequency was.

And so by converting our signals into spectrograms, we're able to use neural networks frameworks that are very efficient for image processing.

And they've been really well-suited for this task because you can't be sending your signals up to the cloud all the time.

It's just too power intensive, right? So you need to have a detector that's both accurate and also lightweight enough to run in real-time.

It also had the benefit of being relatively small.

Every neural network has a parameter count, which is... basically you can think of it as, like, the amount of knobs that you're turning to tune this model in order to better classify whatever you're classifying.