{"id":207,"date":"2020-03-14T13:15:57","date_gmt":"2020-03-14T13:15:57","guid":{"rendered":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s20-teame0\/?p=207"},"modified":"2020-03-23T11:59:48","modified_gmt":"2020-03-23T11:59:48","slug":"ryans-status-report-for-saturday-mar-14","status":"publish","type":"post","link":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s20-teame0\/2020\/03\/14\/ryans-status-report-for-saturday-mar-14\/","title":{"rendered":"Ryan’s Status Report for Saturday, Mar. 14"},"content":{"rendered":"

This week, we’ve worked on creating the FIR filter for converting the 1-bit 1-3MHz Pulse Density Modulation output of the microphone to a standard double precision array in MATLAB. Essentially, the FIR filter is a lowpass filter that only allows signals below 20kHz (human hearing range) to pass through.<\/p>\n

We used MATLAB’s Filter Designer tool to generate a lowpass FIR filter with 1.000MHz sampling frequency, 20kHz passband, 25kHz stopband. The resulting FIR filter had 506 taps. I could have lowered the stop band at the expense of higher filter order, but I didn’t want to slow the system down too much during the actual filtering process. We will see if a sharper filter is needed. The FIR filter has the following frequency and phase response:<\/p>\n

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In order to apply the filter, we perform a convolution of the signal with the coefficients of the filter.<\/p>\n

Original PDM signal, zoomed in:<\/p>\n

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Filtered result:<\/p>\n

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Filtered result, zoomed in:\"\"<\/p>\n

Great, we are getting our 850Hz tone that we applied to the mic back!<\/p>\n

Further considerations:<\/p>\n