WEB APPLICATION GUITAR TUNING USING MACHINE LEARNING WITH ML5.JS

Abstract

The field of machine learning has grown rapidly in recent years and is now widely used to identify patterns in data and make predictions or decisions. In today's fast-paced world, musicians are constantly looking for ways to improve their skills and performance. However, tuning a guitar can be a difficult and time-consuming task, depending on the musician's ears to detect the right note. With modern technology, machine learning algorithms can automate this process, simplifying guitar tuning. Tuning a guitar is a complex process of determining the frequencies of the strings and tuning them to the correct pitch. Standard, Alternate, and Custom Tuning are the three most popular guitar tuning styles, each with their own set of challenges. Using waterfall model method can be more precise to make this project come true. With help from ml5.js library this project can access the microphone and do pitch detection so this project will be running as expected. The result obtained from this study are creation of web-based application of tuning guitar by implement waterfall model method, Fast Fourier Transform Algorithm, anf ml5.js library. The name of this project will be “G-Tune”