Posted on

Eli Status 4/29

What did you personally accomplish this week on the project? Give files or photos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).

I completed implementing pitch detection, note onsets and midi score generation. I am completely done with my parts of the project except for testing and integration.

Final procedure:

  1. Get amplitude envelope for detecting whether a note is being played or not:

2. Run pitch detection and onset detection (rough):

3. Put it all together:

This graph is a little messy, but it is showing filtered pitches (spikes from 2. removed) in blue, note onsets in red, and whether a note is being played or not in green.

Still need to do testing, this is being held up due to wanting to test using our group’s recording procedure. Should be quick to do testing though.

Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?

Just behind on testing, but that will only take an hour or so maximum once the other parts of the project are integrated.What deliverables do you hope to complete in the next week?

Finish integration/testing.

Posted on

Vedant’s status report 4/29/2023

What did you personally accomplish this week on the project? Give files orphotos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).

This week I was helping Sachit get the web application working. We decided to work on it together since I have prior experience with Django and this would probably make integration easier.

Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?

Our progress is on track to be done by the demo

What deliverables do you hope to complete in the next week?

In the next week, I hope to integrate my generative music model with the web app interface

Posted on

Eli Status 4/23

What did you personally accomplish this week on the project? Give files or photos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).

Implemented memoization for the Yin autocorrelation method. This speeds up computation significantly and allows me to take continuous lag windows which fixed the accuracy problem.

This is the output of the autocorrelation currently on a c major scale (frequency on left axis, time on bottom). This is now fully accurate.

I also implemented midi score generation. This is quick, flexible and fast. Generated sample midi scores and tested with Vedant’s model. Seems to be working fine.

Performed initial accuracy testing on a few different downloaded clean tone guitar samples. Accuracy >95% correct identification. The main source of inaccuracy is due to the tuning of the window sizes.

Worked on onset/offset detection. Found a few methods, likely going to stick with the built in aubio onset detection. There is some inaccuracy, but it seems good enough for primer generation:

Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?

On schedule.What deliverables do you hope to complete in the next week?

Integrate and more fully test pitch detection/onset detection.

Posted on

Team status report 04/23/2023

 

  • What are the most significant risks that could jeopardize the success of the project? How are these risks being managed? What contingency plans are ready?

The biggest risk to the project is still the quality of generated music. Although the quality of music generated by the pre trained model is good, It’s hard to predict how the finetuning will affect the generated music. To mitigate this risk we’re researching different techniques used when fine tuning models (such as freezing certain weights) to prevent divergence.

  • Were any changes made to the existing design of the system (requirements, block diagram, system spec, etc)? Why was this change necessary, what costs does the change incur, and how will these costs be mitigated going forward?

The overall design of the system remains the same.

  • Provide an updated schedule if changes have occurred.

No changes

Posted on

Vedant status report 04/23/2023

What did you personally accomplish this week on the project? Give files orphotos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).

This week a lot of time and effort was put into pivoting the implementation of the generative music model. After training a transformer model from scratch I quickly realized that given the time and resources I wouldn’t be able to get the results I need training a model from scratch. Following this I decided to use a pretrained model as a starting point and finetune that model for the different genres we presented in our use case requirements. I decided to use the pop music transformer (https://github.com/YatingMusic/remi) as a starting point and fine tune this model with files from the Lakh MIDI dataset segmented by genre.

Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?

I feel that my progress is on track, using a pretrained model means that models would need far lesser training time which makes up for this pivot.

What deliverables do you hope to complete in the next week?

Fine tune the model on the different datasets for the different genres.

Posted on

Eli Status 4/8

What did you personally accomplish this week on the project? Give files or photos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).

Implemented Yin autocorrelation method from paper.

http://audition.ens.fr/adc/pdf/2002_JASA_YIN.pdf

This autocorrelation method is extremely slow. I changed windowing parameters of the autocorrelation and changed the frequency range over which the autocorrelation operates. Now the speed is acceptable (%70 length of input), but the accuracy needs help. The autocorrelation is finding the second most powerful harmonic peaks rather than the main fundamental peak.

This is the output of the autocorrelation currently on a c major scale (frequency on left axis, time on bottom). The autocorrelation finds relative pitches very well. It sees whole step, whole step, half step, whole, whole, whole, half exactly as a major scale should be. It just thinks the fundamental is different.

Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?

On scheduleWhat deliverables do you hope to complete in the next week?

Continue tuning pitch detection. Include onset detection for note timing and create MIDI score from input.

Posted on

Team Status Report 04/08/2023

  • What are the most significant risks that could jeopardize the success of the project? How are these risks being managed? What contingency plans are ready?

Right now the biggest risk seems to be that the quality of generated music isn’t up to par. We have a few ideas to combat this we are going to discuss with Professor Sullivan.

  • Were any changes made to the existing design of the system (requirements, block diagram, system spec, etc)? Why was this change necessary, what costs does the change incur, and how will these costs be mitigated going forward?

We are now planning to have our web application compatible with mobile phones, this doesn’t incur additional cost as we were already planning to use bootstrap for our front end which dynamically sizes content.

  • Provide an updated schedule if changes have occurred.

No changes

Posted on

Vedant’s status report 04/08/2023

  • What did you personally accomplish this week on the project? Give files orphotos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).

I met my goal for the interim demo which was to have an end-to-end pipeline working for music generation. Training the model took a long time, and I was listening to output samples to see what the model did well and what it didn’t.

  • Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?

Progress is on schedule, now that I have a working pipeline, I need to train three different models for the three different genres.

  • What deliverables do you hope to complete in the next week?

I plan to discuss possibly fine-tuning a pre trained model with Professor Sullivan, and based on that finding techniques to have the model perform better than it currently is.

  • Now that you are entering into the verification and validation phase of your project, provide a comprehensive update on what tests you have you run or are planning to run. In particular, how will you analyze the anticipated measured results to verify your contribution to the project meets the engineering design requirements or the use case requirements?

This is something I have recently been doing research on, initially, the plan was to use metrics such as tonal distance between the generated MIDI and input MIDI, and the percentage of empty bars generated. But I recently came across a research paper that explored other metrics. I will discuss with Professor Sullivan before finalizing

Posted on

Eli Status 4/1

What did you personally accomplish this week on the project? Give files or photos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).

Finished ESP32-A1S bringup. Audio streaming from board done.

Have windowed stfts done. Still need to ensure precision and accuracy of determined fundamental are good. Requires bin size tuning and ensuring the parameters of the transform are well suited for clean tone electric guitar recordings.

Performed filtering of static noise. Able to remove most common electric guitar noise.

Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?

Almost back on schedule. As long as I have pitch detection for the interim demo, I will be on track.What deliverables do you hope to complete in the next week?

Read fundamental pitch from analyzed guitar notes, perform testing to ensure accuracy.

Posted on

Team Status 4/1

  • What are the most significant risks that could jeopardize the success of theproject? How are these risks being managed? What contingency plans are ready?
    • The risks remain the same as last week and they are being managed the same
  • Were any changes made to the existing design of the system (requirements,block diagram, system spec, etc)? Why was this change necessary, what costsdoes the change incur, and how will these costs be mitigated going forward?
    • No changes were made
  • Provide an updated schedule if changes have occurred.
    • No changes