Tablature Notation from Monophonic Guitar Audio Using CNN

Abstract

Automatic Music Transcription for instruments with fretboards, such as the guitar, involves transcribing audio into either standard notation or tablature notation. Tablature notation provides a one-to-one mapping between the symbol for a note and the string-fret combination used to produce it, and is often preferred over standard notation for this reason. Detecting the string-fret combination used to produce a note involves pitch detection and string detection, which are usually performed in this order in existing approaches. This Master's Thesis focuses on electric guitar string detection from monophonic samples using a convolutional neural network (CNN). A dataset containing over 10000 guitar notes with a detectable fundamental frequency was collected from three electric guitars and feature engineered to extract spectrogram, Mel-spectrogram and constant-Q transform per sample. Three convolutional neural networks were trained, one on each feature, to detect the guitar string from which each original sample had originated. The models were subjected to 6-fold stratified cross-validation. A string detection accuracy of 0.932 was achieved with the model trained on the constant-Q transform data.