Statistics and Its Interface

Volume 13 (2020)

Number 3

A Sequential Naive Bayes Method for Music Genre Classification Based on Transitional Information from Pitch and Beat

Pages: 361 – 371



Tunan Ren (Guanghua School of Management, Peking University, Beijing, China)

Feifei Wang (Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China)

Hansheng Wang (Guanghua School of Management, Peking University, Beijing, China)


Due to the rapid development of digital music market, online music websites are widely available in our daily life. There is a practical need to develop automatic music genre classification algorithms to manage a huge amount of music. In this regard, the transitional information contained in pitches and beats should be very useful. Particularly, the transition in pitches produces a melody, and the transition in beats produces a rhythm. They both decide the music genre. To take these valuable information into consideration, we propose here a sequential naïve Bayes method for music genre classification. This method can be viewed as an novel extension of the classical naïve Bayes classifier, but takes the transitional information between pitches and beats into consideration. To reduce the number of estimated parameters, we propose a BIC-type criterion and develop a computationally efficient algorithm for model selection. The selection consistency of the BIC method is theoretically proved and numerically investigated. The finite sample performance of the proposed methods are assessed through both simulations and a real music dataset.


BIC, Music Genre Classification, Pitch and Beat, Selection Consistency, Sequential Naive Bayes

Received 9 October 2019

Accepted 8 February 2020

Published 22 April 2020