Embedded hardware, software, and FPGA design at DornerWorks, Ltd. located in Grand Rapids, Michigan. M.S.E.E. at University of Tennessee and one year as Ph.D. student at Tennessee Technological University. Interests range from PCB layout to machine learning.

Multilayer Perceptrons and Event Classification with data from CODEC using Scilab and Weka

David E Norwood November 25, 2015

For my first blog, I thought I would introduce the reader to Scilab [1] and Weka [2].  In order to illustrate how they work, I will put together a script in Scilab that will sample using the microphone and CODEC on your PC and save the waveform as a CSV file.  Then, we can take the CSV file and open it in Weka.  Once in Weka, we have a lot of paths to consider in order to classify it.  I use the term classify loosely since there are many things you can do with data sets...


Re: Machine learning and detection in noise

Reply posted 11 months ago (06/29/2019)
You could also consider an FPGA vs. a GPU, specifically the Ultra96 and Xilinx DNNDK. GPUs are great for training, but you can get see better results (latency and...

Re: Machine learning and detection in noise

Reply posted 11 months ago (06/28/2019)
I assume by "set of parameters or coefficients" you are referring to features of each waveform that would be used in a "neural network." With that, I would expect...

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