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If you want to use machine learning for such a project, you can use vibrations data directly, and treat the problem as a regular audio classification problem. A simple approach would be to use a Neural Network with Convolutions. This would take care of features extraction for you. And maybe follow these by dense layers at the end. Given that, it would be ...


by svm do you possibly mean singular value decomposition (svd a known noise reduction technique) if this is true then i would say the next method i would try would be wavelet transform for noise reduction and if neither of these techniques are working on there own it is not uncommon to use them together as is done here.


This might be more of a signal-processing question, rather than a artificial intelligence question, but I will try my best to be of help. Do you know what the noise you are trying to remove is? How it behaves/where it stems from? Or, do you know how your output signal should look, post processing? If you know these things and you are familiar with MATLAB ...


A high-level solution is to use the NVIDIA DriveWorks SDK. NVIDIA's Developer Blog has a good post, "Point Cloud Processing with NVIDIA DriveWorks SDK". The NVIDIA DriveWorks SDK contains a collection of CUDA-based low level point cloud processing modules optimized for NVIDIA DRIVE AGX platforms. The DriveWorks Point Cloud Processing modules include common ...

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