Met with the team and prepared for presentation. I did practice for the design presentation by reading over all our content, ensuring it contained all required information and trying to internalize everything so that I could talk to the audience without having to barely read the slides (except for maybe reading specific numbers for data).
I looked into how we can detect the SNR of our signal by doing some research online. It turns out SciPy used to have a function that calculated the SNR of a signal, but it was deprecated. However, the code for that function is still online and therefore we can use this to detect the SNR in our audio input validation system (here).
I also researched how the rhythm processor should work and how we might want to implement it (paper). After reading some articles, I figured it would be best to try to detect the onsets in a given audio signal to detect the start of a new note. Since we will be dealing with piano music, this makes sense since the onset of a new note will be instantaneous.
I decided to use the find_peaks() function from SciPy. I will be iterating over the input signal in the time domain, by looking at sections of the signal of a certain length (as of right now we said 1/8th of a beat) and trying to find peaks there. If I find a peak in a section I will append a 1 to the found peak array and if not I will append a 0. That is the main idea of the algorithm, which will return an array of 1s and 0s which indicate if there is a peak in that section of the signal or not (so the value of index 0 would be the first section of the signal, the value at index 1 the second section of the signal, etc).
The code right now looks something like this, but it is still being tested as the find_peaks() function from SciPy is a little complex since it will require us to come to an agreement on what values to use for the parameters to make it work correctly. I will attach the code I wrote to write the algorithm and test it at the same time.
It can also be viewed here