Month: October 2023

This week started with helping Caleb prepare for the Design Review Presentation. This involved finishing up the slides for the presentation, and creating a thorough and informative block diagram. After Caleb presented, we started working on the Design Report. I wrote outlines and subsections for the Architecture and Principles of Operation section, Use-Case Requirements section, and the Design Requirements section.

On the technical side, I looked at more research papers regarding Dynamic Time Warping, specifically papers written by Dr. Roger Dannenberg. One paper that I looked very closely at is his paper regarding polyphonic audio alignment: Polyphonic Audio Matching for Score Following and
Intelligent Audio Editors (https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=5fd3ed7924505b35f14dbc1bad99155ae97e8655). Here, Dr. Dannenberg explored using Chroma Vector Analysis for differentiating different pitches among different instruments playing at once. This is something that I want to explore more because this could make our audio alignment program more robust. My team and I will be meeting Dr. Dannenberg on Monday, to ask more about his Chroma Vector implementation for audio alignment.

Additionally, I worked on the frequency filtering that we will be using for filtering our signal so that it is more smooth and has less noise, but also for the instrument filtering. I looked at MATLAB documentation of various digital filters that can be used for frequency filtering.  https://www.mathworks.com/help/signal/ug/practical-introduction-to-digital-filter-design.html

So far, my progress is on schedule, and next week I plan to look more into DTW and frequency filtering.

This week I spent time rehearsing for the design review presentation and ensuring my presentation was clear, concise, and coherent. The presentation came with feedback on points I missed and points that helped keep the audience’s attention.

On the technical side, I spent the week working on implementing Dynamic Time Warping (DTW). This is just a rough sketch of the function because the function needs to be adjusted based on whether the input is a MIDI file or list of points or numpy array. Then the function can also be improved by adding chroma vectoring which involves inputting vectors of length 12 where each dimension corresponds to a pitch (i.e C, C#, D, etc). In other words, the input becomes a vector containing how much of each pitch was present. This is important if the player plays more than one note at a time called an interval or a chord. This may be able to be further improved by changing the input to a 24-input vector, allowing the input to span across more than two octaves. However, this may lead to more latency and compromise a use-case requirement.

This upcoming week, we look forward to talking to Roger Dannenberg, the creator of Audacity. His expertise in computer music and music analysis will be really helpful for our project. Not to mention, Dr. Dannenberg has built a system similar to ours that used only audio, so we aim to pick his brain on how he was able to accomplish this. We also wonder what he thinks about the ability of eye-tracking to improve the rate at which page flips feel comfortable. For personal goals, I aim to test out frequency filtering and how much this affects the ability to align to MIDI files where those high frequencies are not filtered.

We are currently still on track and will continue to work hard to stay on track.