Automated thresholding in electric guitar note recognition using Fourier analysis and neural network

Authors

John Voltaire S. Tensuan ⋅ PH Physics Department, De La Salle University
Al Rey Villagracia ⋅ PH Physics Department, De La Salle University
Romeric F. Pobre ⋅ PH Physics Department, De La Salle University

Abstract

Writing manually the musical notes on a tablature while playing a musical instrument is time-consuming for the musicians in composing songs.
To resolve this problem, this research paper generates a real-time automated transcription system that will write the musical pieces on a computer-generated tablature while playing the guitar. This is done through recording the wave data from an electric guitar by a computer and undergoes Fourier transform. The transformed data is fed into a neural network which is trained to recognize the pattern of the data using the backpropagation algorithm. Each network is assigned to different chords to recognize them respectively. Given the input data, the network will automatically compute for a threshold value which can range from 0.8 to 0.9 to determine the musical note from the trained neural network.
Finally, the automated transcription software which was compiled and built in Microsoft Visual Basic and C++ will display the played notes in a tablature as the musician plays the guitar.

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Issue

2006: Proceedings of the 24th Samahang Pisika ng Pilipinas National Physics Congress

Article ID

SPP-2006-PA-11

Section

Poster Session PA

Published

2006-10-25

How to Cite

[1]

JVS Tensuan, AR Villagracia, and RF Pobre, Automated thresholding in electric guitar note recognition using Fourier analysis and neural network, Proceedings of the Samahang Pisika ng Pilipinas 24, SPP-2006-PA-11 (2006). URL: https://proceedings.spp-online.org/article/view/SPP-2006-PA-11.