Development of a Novel Method for Automatic Detection of Musical Chords

Samin Yaseer Mahmud (), Farjana Snigdha () and Adnan Siraj Rakin ()

Scientific Modelling and Research, 2018, vol. 3, issue 1, 15-22

Abstract: This paper represents a method to extract guitar chords from a given audio file using a probabilistic approach called Maximum likelihood estimation. The audio file is split into smaller clips and then it is transformed from time domain into frequency domain using Fourier Transformation. There are multiple known frequencies of musical notes we denote them as reference frequencies. A chord basically is a combination of multiple frequencies. Fourier transformation allows us to identify the frequencies that have precedence over other frequencies in that clip. So, we identify the frequencies having precedence over other frequencies and match them with the reference frequencies to find out which note they belong to. Thus, we get a number of notes in each clip yielding us a specific chord. If we fail to obtain a chord for any sample clip, we follow a probabilistic approach which is termed as ‘Maximum Likelihood Estimation’ and we use it to approximate the musical chord for the first time with high level of accuracy.

Keywords: Notes; Chord; Fourier transformation; Maximum likelihood estimation. (search for similar items in EconPapers)
Date: 2018
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