Deeya’s Status Report for 3/8/25

I mainly focused on my parts for the design review document and editing it with Grace and Shivi. Shivi and I also had the opportunity to speak to flutists in Professor Almarza’s class about our project, and we were able to recruit a few of them to help us with recording samples and providing feedback throughout our project.  It was a cool experience to hear about their thoughts as well as understand how this could be helpful for them during practice sessions they have. Specifically for my parts of the project I continued working on the website and learned how to record audio and store it in our database to be used later. I will now be starting to put more of efforts in the Gen AI part.  I am thinking of utilizing a Transformer-based generative model trained on MIDI sequences and I will need to learn how to take MIDI files and convert them into a series of token encodings of musical notes, timing, and dynamics, so that it can be processed by the Transformer model. I will also start compiling a dataset of flute MIDI files.

 

Team Status Report for 2/22/25

This past week we presented our Design Review slides in class and individually spent time working on our respective parts of this project. Deeya is almost done completing the basic functionality of the website and has so far completed the UI of the website, user profiles and authentication, and navigation of different pages. Grace and Shivi are planning to meet up to combine their preprocessing code and figure out the best way to handle audio segmentation. They want to make sure their approach is efficient and works well with their overall pipeline that they are each creating on their own.

This week we will focus on completing our design report, with each of us working on assigned sections independently before integrating everything into a cohesive report. We are planning to finish the report a few days before the deadline so that we can send it to Ankit and get his feedback. This Wednesday we will also be meeting with Professor Almarza from the School of Music and his flutists to explain our project and to come up with a plan on how we would like to integrate their expertise and knowledge into our project.  

Overall we each feel that we are on track with our respective parts of the project and we are excited to meet with the flutists this week. We haven’t changed much to our overall design plan and there aren’t any other new risks we are considering besides the ones we laid out in our summary report last week. 

Deeya’s Status Report for 02/22/2025

This week I finished setting up the user authentication process for our website so that each user will have an associated profile to their account. This will help keep track of what transcriptions belong to which user and which transcription to upload in their respective Past Transcriptions page. I also started looking into how to record live audio through the website and store that in our database so that it can be used by the pitch and rhythm algorithm being designed by Grace and Shivi. Overall I am on track with the website and should be done with its overall functionality this week. One thing I still want to figure out is how to take what is most recently stored in our database of either the uploaded or live recorded audio files and automatically put that through the pitch and rhythm algorithms so that when it is time to integrate the process should be smooth. For the Gen AI portion of the project it looks like I might need to create a labelled dataset myself which I will have time to focus on once I finish up the website this week. Also for this week I will be working on my portions of the design review report.

Deeya’s Status Report for 2/15/25

This week, I made progress on our project’s website by setting up a Django application that closely follows the UI design from our mockup using HTML and CSS. I am finishing up implementing the user authentication process using OAuth, which will allow users to easily register and log in with their email addresses. User profile information is being stored in an SQL database. I am currently on track with the website development timeline and will be focusing next on being able to upload files and storing them in our database. Also I will begin working on the “Past Transcriptions” web page, which will show the user’s transcription history along with the dates each transcription was created.

Regarding the generative AI component of the project, I am still searching for a large enough labeled dataset for training our model. I found the MAESTRO dataset of piano MIDI files, which would be ideal if a similar dataset existed for the flute. If I am unable to find a large labeled dataset within the next few days, I am planning on creating a small dataset myself as a starting point. This will allow me to start experimenting with model training and fine-tuning while continuing to look for a better dataset.

Deeya’s Status Report for 02/08/25

  • I am tasked with working on the web/mobile application part of our project as well as with implementing the Gen AI aspect of our project
  • We first were trying to assess whether a web application or a mobile app would work better for our project and its use cases. We decided to use a web app instead because it is easier to access, upload and store files, and authenticate users, and we overall have more experience working with Python, Javascript, HTML, CSS than with Swift for iOS apps. 
  • I designed a very basic UI for the website and will be starting a Django project that has the UI and basic functionality like being able to upload/save files to a database and has user profiles to allow users to login and out.
  • For the Gen AI component the first step is to find a large enough dataset of flute music of different genres. I spoke with Professor Dueck to ask her if there was CMU archival of flute music or any resources that she recommends to look through. She recommended looking at classicalarchives.com and specifically for solo or duet flute sonatas or anything unaccompanied. Looking through this website there are a lot of flute compositions that can be useful for this project. However I still need to figure out what would be the best way to compile together a large dataset and categorize/label each piece based on its genre, tone, pace. This will be a time consuming process so I will still continue researching for more flute labelled datasets.