Blog Post #5

Hello, and welcome to the fifth blog post of my senior project! In this post, I will finalize my preprocessing for the GuitarSet dataset.

Last week, I focused on creating the Transfrom class with the necessary preprocessing methods to turn each individual audio file into spectogram data with string-fret annotations. This week, I focused on continuing the work we started last week with GuitarSet. The processAudio method I created last week implements the computeSpecotgram method and returns an numpy array with the spectogram data and a dictionary with the chord annotations, which can be called on any individual audio file. Now, in a new class I created a run method to take an audio file and call the processAudio function in order to get a numpy array of spectogram data and a dictionary of chord annotations. Then I created a combineResults function which takes the array of spectrogram data and dictionary of string-fret annotations and combines them in a tuple. The Gutiarset dataset has around 360 audio files and I wanted to take .2 seconds of audio from each audio file to preprocess and then add to a final dataset. My Main class loops through the audio files in Guitarset and calls the driver methods on each single audio file and adds it to a tuple. With this, my dataset is fully preprocessed

Next week, we move on to planning my next steps and modeling. Stay tuned!