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MITJANS COMA ALBERT_1338767_assignsubmission_file_TFG-Mitjans-Coma-Albert.pdf (14.52 MB)

Document type

Bachelor thesis

Publication date

2020-06

Publication license

cc-by-nc-nd (c) Mitjans, 2020
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/171413

Visual recognition of guitar chords using neural networks

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Abstract

In this paper, we use deep learning to study high-level features for guitar chord detection. In particular, the goal of this project is to build a neural network capable of recognizing finger patterns on the frets of a guitar. Given an input image, the network is able to identify fingers, frets, strings and the corresponding chord. Using a 2-stack Hourglass network for the detection and applying a post-processing algorithm to its corresponding output heatmaps, a 97% accuracy on the detection of chords of 205 different images is obtained

Description

Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2020, Tutor: Artur Carnicer

Subject

Música per a guitarra, Xarxes neuronals (Informàtica), Treballs de fi de grau

Subject (English)

Guitar music, Neural networks (Computer science), Bachelor's theses

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Treballs Finals de Grau (TFG) - Física

Citation

MITJANS COMA, Albert. Visual recognition of guitar chords using neural networks. [consulted: 9 of August of 2026]. Available at: https://hdl.handle.net/2445/171413

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