Blog Post #6

Hello, and welcome to the sixth blog post of my senior project! In this post, I will debug some issues that came up with my pre-processing and start building my model architecture.

Last week, I finished preprocessing GuitarSet and was ready to move on to building my model. However, after running some tests this week, I noticed that my preprocessing methods only worked for certain tracks and not others. For some audio files, it would simply repeat the same note positionings for each string on the guitar. After debugging this, we were ready to move on to the modeling stage of the project. Because the debugging took a substantial amount of time, we took the rest of the week to plan out the modeling steps for the following week. Since our model will need to take in visual inputs through our spectrograms, we opted to use a convolutional neural network (CNN). Our model’s output will be a string-fret combination represented by a 6 × 19 matrix, where 6 stands for a conventional guitar’s six strings and 19 for the 18 frets plus an additional space when a string is not being played. We decide to use a DNN architecture known as the multitask learning architecture to get this kind of output from a deep learning model. We plan to make our architecture branch out into 6 endpoints with each endpoint producing a vector size of 19.

Next week, we move on to implementing these steps and creating our model. Stay tuned!