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This is an awesome topic, but I'm somewhat annoyed they didn't dive into what kind of DSP and instead turned the article into an advertisement. Does anyone hav

This is an awesome topic, but I'm somewhat annoyed they didn't dive into what kind of DSP and instead turned the article into an advertisement. Does anyone have any good further reading on the topic? (Books, articles, classes, anything really.)

bsmitty5000 7y ago
https://terpconnect.umd.edu/~toh/spectrum/IntroToSignalProcessing.pdf https://terpconnect.umd.edu/~toh/spectrum/IntroToSignalProce... https://allsignalprocessing.com/category/lesson-categories/ https://allsignalprocessing.com/category/lesson-categories/ https://www.digikey.com/en/resources/edu/arm-education-media/dsp https://www.digikey.com/en/resources/edu/arm-education-media... http://www.eas.uccs.edu/~mwickert/ece5650/ http://www.eas.uccs.edu/~mwickert/ece5650/ http://www.eas.uccs.edu/~mwickert/ece5615/ http://www.eas.uccs.edu/~mwickert/ece5615/ http://www.naiant.com/SM/SM%20Forum%20Attachments/Other%20documents/926_Music%20DSP.pdf http://www.naiant.com/SM/SM%20Forum%20Attachments/Other%20do... Sorry I don't know if any of these are good. They're in my dsp bookmarks directory, I think I found these a while back when I was trying to relearn these topics.
Optimal_Persona 7y ago
The best DSP intro I've found, especially for the non-math whiz is Steven W. Smith's DSP Guide: http://www.dspguide.com/pdfbook.htm http://www.dspguide.com/pdfbook.htm
- proverbialbunny 7y ago
  I own a copy of this book. It is quite good as a reference once one has some DSP experience. I wouldn't recommend it as an intro. The book is quite good in that it is to the point and provides a good roadmap, but to do so it often omits concrete examples and instead writes out the algorithms in mathese. Knowing calculus should be enough.
janjongboom 7y ago
Author here. Our main focus right now is on vibration and non-voice audio. For vibration we use spectral analysis through butterworth filters, FFT over the result, looking at the peaks and locations of the FFT result, and then at spectral power buckets. For audio we look at MFCC (sometimes paired with a bandpass filter). The source for these are in https://github.com/edgeimpulse/processing-blocks https://github.com/edgeimpulse/processing-blocks (Python) and https://github.com/edgeimpulse/inferencing-sdk-cpp https://github.com/edgeimpulse/inferencing-sdk-cpp (optimised C++). What’s though on our end is that we need to run the DSP in real-time on very constrained devices. Unfortunately I have very little good resources on learning more. I’ve done lots of experimentation in Matlab, just getting raw sensor data and playing with the signal processing toolkit that they sell.

bsmitty5000 7y ago

https://terpconnect.umd.edu/~toh/spectrum/IntroToSignalProcessing.pdf https://terpconnect.umd.edu/~toh/spectrum/IntroToSignalProce... https://allsignalprocessing.com/category/lesson-categories/ https://allsignalprocessing.com/category/lesson-categories/ https://www.digikey.com/en/resources/edu/arm-education-media/dsp https://www.digikey.com/en/resources/edu/arm-education-media... http://www.eas.uccs.edu/~mwickert/ece5650/ http://www.eas.uccs.edu/~mwickert/ece5650/ http://www.eas.uccs.edu/~mwickert/ece5615/ http://www.eas.uccs.edu/~mwickert/ece5615/ http://www.naiant.com/SM/SM%20Forum%20Attachments/Other%20documents/926_Music%20DSP.pdf http://www.naiant.com/SM/SM%20Forum%20Attachments/Other%20do... Sorry I don't know if any of these are good. They're in my dsp bookmarks directory, I think I found these a while back when I was trying to relearn these topics.

Optimal_Persona 7y ago

The best DSP intro I've found, especially for the non-math whiz is Steven W. Smith's DSP Guide: http://www.dspguide.com/pdfbook.htm http://www.dspguide.com/pdfbook.htm

proverbialbunny 7y ago
I own a copy of this book. It is quite good as a reference once one has some DSP experience. I wouldn't recommend it as an intro. The book is quite good in that it is to the point and provides a good roadmap, but to do so it often omits concrete examples and instead writes out the algorithms in mathese. Knowing calculus should be enough.

janjongboom 7y ago

Author here. Our main focus right now is on vibration and non-voice audio. For vibration we use spectral analysis through butterworth filters, FFT over the result, looking at the peaks and locations of the FFT result, and then at spectral power buckets. For audio we look at MFCC (sometimes paired with a bandpass filter). The source for these are in https://github.com/edgeimpulse/processing-blocks https://github.com/edgeimpulse/processing-blocks (Python) and https://github.com/edgeimpulse/inferencing-sdk-cpp https://github.com/edgeimpulse/inferencing-sdk-cpp (optimised C++). What’s though on our end is that we need to run the DSP in real-time on very constrained devices. Unfortunately I have very little good resources on learning more. I’ve done lots of experimentation in Matlab, just getting raw sensor data and playing with the signal processing toolkit that they sell.