I know google has general sound classifiers like Yamnet, trained on youtube data but they are not very good for specific usecases. So you would have to create a custom model for you usecase.
just thinking, I've talked with a mechanic but he told me that now when they connect the car to a computer they almost always find anything wrong with a car, that and the experience they have they almost always know what's wrong.
I think sound + location could be really interesting, because you can filter parts of the car that could be making noises that are similar knowing where the mic is.
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Do you think the same could be used for other cases? I'm thinking about detecting problems with cars (vehicles) just by the noise they make
I know google has general sound classifiers like Yamnet, trained on youtube data but they are not very good for specific usecases. So you would have to create a custom model for you usecase.
- https://www.tensorflow.org/hub/tutorials/yamnet
How far has your thinking taken you? This has piqued my interest on how many fields audio can be used to solve problems
just thinking, I've talked with a mechanic but he told me that now when they connect the car to a computer they almost always find anything wrong with a car, that and the experience they have they almost always know what's wrong.
I think sound + location could be really interesting, because you can filter parts of the car that could be making noises that are similar knowing where the mic is.