Empirical Analysis of Sigmoid Calibration on Gaussian Naïve Bayes Performance for Guitar Chords Classification
Kalyandurg Rafeeq Ahmed, Anu Malhan, Priyadharshini Ganapathy Prasad, Swati Sharma, M. Vanitha Lakshmi, Nipun Sharma · 2025
Music has been an age-old phenomenon and is cherished and enjoyed across the global boundaries and borders. One important aspect of music has been learning it. With the advent of technology music learning has become accessible to almost everyone with virtually little or no infrastructural requirements at all. Mobile applications specific to a musical instrument learning are developed and are continuously evolving for providing a learning experience as real as in person face to face learning. Guitar learning is one of the most popular instruments playing among all the age groups. Fundamentals of guitar playing and learning often includes the knowledge about guitar strings and chords etc. Going one step deeper, the domains like music recognition and music recommendation systems are evolving at a very fast pace.In this paper, we have worked on Guitar chord classification problem which has been explored and experimented in more than one dimension for music recognition and recommendation models. The preprocessing of guitar chords audio into numerical data is computationally extensive. During the preprocessing stage the harmonics are generated in large numbers and the effect of higher order harmonics is explored in some previous works. In this paper we have evaluated the Brier score losses of the Gaussian Naïve bayes algorithm for guitar chord classification problem for two sets of harmonics count. Further, the sigmoid calibration is done on the classification problem and the experimental results are obtained and presented in the results and discussion section.