Musical Guitar Tuning using Machine Learning Approach for Custom Data using FFT and MFCC

The proposed model explores the application of machine learning to automate the tuning process for musical instruments, focusing especially on guitars. this approach involves extracting detailed audio features from the guitar using Mel-Frequency Cepstral Coefficients (MFCC) and Fast Fourier Transform (FFT). MFCCs capture the timbral characteristics of the audio signal by analyzing its short-term power spectrum. This method provides a detailed representation of the phonetic attributes essential for musical note identification. By using MFCC, It can effectively capture the nuances of each string's sound and tone quality. FFT transforms the audio signal from the time domain into the frequency domain, permitting us to examine its frequency components and harmonics. This transformation is very crucial for distinguishing between the fundamental frequencies and detecting deviations from standard tuning. The features extracted from MFCC and FFT are then apply random forest machine learning algorithms to train models for detecting tuning nuances. These models are designed to recognize deviations from the expected pitch and provide corrective adjustments, ensuring that each string is tuned precisely. system processes the audio signal to maintain its original quality, making adjustments to align each string perfectly with its intended pitch. This model demonstrates the combining of MFCC and FFT with machine learning techniques and offers a simple solution for automated guitar tuning. The Accuracy vs number of trees was found to be 92.11% and that of Loss vs number of trees is 7.89%. The overall tuning accuracy of all the strings was found to be 98.27%.