Introduction
The creation of music is not so much a problem to be solved so much as it is a creative expedition. At the end of the day, having a machine produce any quality of music is a success for this project. Our reference is a paper from Stanford, and they used an end-to-end learning model to generate classical music. We’re not experienced with end-to-end learning, as it involves significantly more difficult and complicated mathematical formulation than the traditional neural networks we’ve learned about and implemented. Instead, we will be using a generative adversarial network to achieve the same goal, which will hopefully produce comparable results to those observed in the paper. The three of us are all quite musically inclined, with a particular interest in piano and guitar music.
Challenges
What has been the hardest part of the project you’ve encountered so far? The data we are using is a NumPy distribution of MusicNet. It was in npz format and it took us a little time to figure out how to extract the data that we needed out of it. Additionally, as we were working on our data, and contemplating how to utilize it for our purposes of generating our own tunes, we realized that our previously planned GAN model wouldn’t be the best fit. Instead, we have decided to employ the LSTM-based RNN model.
Insights
Are there any concrete results you can show at this point?
We are currently saving each song to its own .npz file for easy loading in the future. This will take a long time, but we don’t need too many song files to properly train our model. Additionally, by this token, we have some music files that are one-hot vectors available to be viewed.
How is your model performing compared with expectations?
Our model is not yet being trained, but we are not concerned.
Plan
Are you on track with your project?
Even though we haven’t finalized our model, we have agreed on the details of our model. We will be employing an RNN model based on LSTM. We may not be on track with the entirety of the expectations for this deliverable, but we can see our roadmap until the due date and feel quite confident about the further steps. As a result, we are happy with the process we’ve done.
What do you need to dedicate more time to?
I think we need to dedicate more time to building our model, and testing its efficiency, then we can work on our methods to improve our efficiency.
What are you thinking of changing, if anything?
We have already decided to switch from a GAN to an LSTM based approach.
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