However, the model’s ability to correctly tab audio snippets is a fantastic development. Also, since chords can have different variations each containing the same notes, the model does not recognize when to use a specific voicing - which may prove inconvenient - but is not a significant problem. The current model does not yet take into account the duration a note is held and will continue to repeat the tab for the duration specified in the code. This model is not yet ready to begin creating full length guitar tablature as a couple of issues still linger. Which resulted in an average accuracy of 84.23%. The entirety of the GuitarSet data set audio files was not used for this model, however, a sufficient number of input files were used totaling 40828 training images and 4537 test samples. Dropout layers are added to reduce overfitting. For each of these six outputs, a softmax activation along with a categorical cross-entropy loss function are applied. The model features six tasks (eBGDAE strings) to determine if the string is not played, open, or a note is being played. The Keras functional API was used to create the following multi-task classification model with a 90/10 split in training and test data. When training, this matrix is broken up into six separate arrays to train each head of the model. The first column identifies whether that the string is not being played, the second column identifies if the open string is being played, and the third through nineteenth columns identify the specific fret that is being played starting from the first fret. Each matrix shape is (6, 19) where the six rows correspond to each guitar string (eBGDAE from top to bottom). The above matrix is the guitar tab solution for one random 0.2 second selection from the GuitarSet data set. The previous code snippets return data such that the output is similar to a one-hot encoding of categories, the following matrix format was returned for each 0.2 seconds of audio: ] This is done so the softmax function can still choose a category for strings without a note being played. The solution with the lowest ‘finger economy’ number is chosen as the correct chord shape.Īdditionally, in the first column for each row, if there exists a note (1 in the row), a zero is appended and vice versa if a note does not exist. The idea of ‘finger economy’ is created - the lowest note of the chord, the root note, is compared to the rest of the notes in the chord where the number of frets (disregarding the string) each note is from the root note is summed to create a ‘finger economy’ number. In order to understand the benefits of using the Constant-Q transform over the Fourier transform to select frequencies and create our input images, we must examine how musical notes are defined:įirst, a matrix (6, 18) of MIDI values which represents the six strings and 18 frets of a guitar is created under variable Fret: ]Īll possible locations of the unique notes retrieved on the guitar were then determined using Fret, where the matrix below shows a possible solution: ]Īll possible solutions for the combination of frets and strings must be determined. It is this functionality of CNNs that we want to harness to output the guitar tab therefore, it is first necessary to transform the input audio files into spectrogram images using the Constant-Q transform. If you are familiar with Convolutional Neural Networks (CNNs), then you might have heard about their potential for image processing and analysis used for Computer Vision. Please note that much of the direction in this project was provided by a research poster from NEMISIG 2019 found here. For training, the GuitarSet data set is employed for its large quantity of isolated guitar recordings with corresponding tabs. This post outlines the implementation of automatic guitar transcription from audio files using Python, TensorFlow, and Keras as well as details the surface level methods performed.
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