Accelerating real-time music beat tracking based on hidden Markov model by using genre information

Abstract

The essence of enjoying music is that people can track music beat anytime and be brought into the scene expressed by music. Music beat tracking is a common task in Music Information Retrieve (MIR). While numerous studies have been done in this field, most works focus on the offline beat tracking. However, tracking music beat in real time is a challenging task for computers. In the past few years, people attach more importance to this field. Researchers care about the music beat without taking music style or context into consideration. In this paper, we propose a method for tracking music beats in real time in conjunction with music genre. Specifically, the proposed model is based on a widely-used framework of Hidden Markov Model (HMM). By recognizing the genre of input music, we narrow the range of beats per minute (BPM), which significantly reduces the number of hidden states in HMM. Consequently, the inference time of beat tracking decreases. We experimentally verify the model on the open-source Ballroom dataset, and its accuracy remains at a competitive level while having a much shorter inference time.