Blog Post #4

Hello, and welcome to the fourth blog post of my senior project! In this post, I will go over my progress on preprocessing the GuitarSet dataset.

This week, I focused on continuing the work we started last week with GuitarSet. In a Google Collab notebook, I loaded in the dataset and the necessary libraries to work with it. Then, I created a Transform class with methods to load an audio file from the dataset, extract the string-fret combinations, and compute the audio data into spectrograms. The most important method, computeSpectogram, works by taking the audio signal and sample rate of the musical data and uses a Constant-Q-Transform to convert the audio into a spectrogram or the visual representation of the frequency of the sound. Then, the method extracts the MIDI values from the audio, which represent the string-fret combinations of the chords being played. Together, this method takes an audio file and outputs a visual representation of the data aligned with the chord that the audio file is identified with. The next method, processAudio, implements the computeSpecotgram method and returns an numpy array with the spectrogram data and a dictionary with the chord annotations.

Next week, I will focus on using these preprocessing functions to process the full GutiarSet dataset and turn all of the preprocessed data into a single new dataset ready for model training. Stay tuned!