The most significant risk for our project right now is the timeline. We have a very strict timeline where we aim to complete much of the research and outlining process for the signal processing within the next week (two weeks total including this week). While we feel it’s a realistic goal, we have found that the process can be very time-consuming and our completion of our research for this week was nearly overdue. The difficulties can compound when it comes to implementing the research in the back-end of our web app, because we have to format the outputs in a way that the computer can process, not simply charts and visual representations that we have been working with for now. There are also elements of our signal processing design that may require further work when we reach the coding stage, as Python has limited signal processing libraries compared to MATLAB, where most of our research is done. Aditya found that after determining the parameters of the STFT of the test signal in MATLAB, he had to work out a different set of parameters while working with the SciPy library’s stft() method.
As we have only just completed the design process process and currently we are on track with our schedule of implementation, we have not required any changes to the existing system design.
These are some pictures of the frequency algorithm detector Alejandro found in Matlab to detect frequencies. The first image shows an audio of a piano playing the c-scale and the second one is a constant c note. We can see the x-axis being the time doing and the y axis the frequency domain.
Our project includes considerations for education and economics. We realize that a lot of people might want to have access to a free easy to use music transcriber. Most transcribers out there come in the form of applications and require subscriptions to them to be able to use them. With our webapp anyone could use it for free. It would especially be useful for teachers who might want to show students the transcription of a specific song they are playing in class for example. It would also make it efficient for people to have a tool that can transcribe short monophonic audios for them, instead of just having to manually transcribe it themselves. Finally, it increases accessibility to music, especially those who may not have the time, financial resources, and other barriers. This may be especially helpful to students and teachers in low-income communities as often their arts and music programs are the first to get cut.