Predictive real-time beat tracking from music for embedded application

Abstract

Beat tracking from music signals has significant importance in multimedia information retrieval systems, especially in cover song detection. A predictive real-time beat tracking system can also be used to assist musicians performing live. In this paper we present a real-time beat tracking algorithm, fast enough to be implemented on an embedded system. The onset of a note is detected using a maximum filter approach that suppresses the effect of vibrato. Beats are predicted a second in advance using a causal variant of Dynamic Programming. We have employed an onset memoization algorithm, to reduce the computational resources required. Raspberry Pi was chosen as our preferred development board. We have demonstrated through experimental results that the proposed approach can satisfactorily estimate beat positions from a music signal in real-time with an average continuity score (AMLt) of 0.67.