To validate the acoustic performance of a product, we are using hand-engineered features and thresholds. Everytime a new hardware problem arises we have to at least tweak a parameter and at worst add a new feature with its own parameters.

We now have tens of thousands of records, with some labeled, and I'm thinking it might good to switch to a completely learned test to just have to add new labeled data when new faults are detected or even better ask for labeling when something unusual is detected.


The speaker plays a sine sweep of 1 sec recorded by several microphones. Here's what its magnitude looks like in the frequency domain:

A good sample

If the speaker were perfect, we would have only the first ramp, but this one is already fairly good.

Here are just a few examples of the kind problems that occur:

  • a badly glued wire that resonates at a particular frequency

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  • a huge harmonic distortion, on a particular range or everywhere

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  • an engineer who decides to replace a drop of glue by a screw

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  • (rather low) external noises: an operator who sneezes while another one relates its week-end, a machine running etc.

Possible difficulties

  • an extremely low false negative rate
  • compute power and memory are scarce resources since the test is run by the product itself


First of all, do you think we should stick with our current approach ? I can't really afford to ask for a few weeks of work on this path if that's a dead end.

I'm thinking about treating the subject as being an image classification problem.

  • Would you treat the whole records at once or slice them in peaces (since things like harmonic distortion have the same "shape" whatever the frequency) ?
  • I'd like an excuse to play with Boltzmann machines and deep neural networks, do you have the feeling that it's not a good direction to go ?

Maybe I'm in the wrong place to ask such an open and non-technical question...


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