Comparative Analysis of Multinomial Naïve Bayes with Categorical Naïve Bayes for Guitar Chord Classification
Nipun Sharma, Swati Sharma · 2025
Guitar chord classification problem is categorical in nature and using variants of Naïve Bayes on such problems hold much promise. Guitar chord classification problem has been at the centerstage for music recognition, recommendation and learning applications. Although the preprocessing of guitar chords audio files into numerical data is computationally very 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 too. Including all the harmonics in the learning stage can lead to dimensionality explosion and later on the need for dimensionality reduction is required. To avoid this situation the effect of higher order harmonics is evaluated and if they are not impactful enough, they can be left out as features. In this paper we have evaluated and compared two versions of Naïve bayes algorithm for guitar chord classification problem for two sets of harmonics count. Multinomial Naïve bayes is compared with Categorical Naïve bayes for datasets of guitar chords with harmonics count from 4th to 9th . Further, the classification problem and the experimental results are obtained and presented in the results and discussion section for clearer comprehension.