Rhythmic Pattern Extraction by Community Detection in Complex Networks

In this paper, we study musical knowledge extraction and discrimination. Specifically, we propose a method for automatic extraction of drums rhythmic patterns of music and the rhythmic summarization of a set of songs from the same artist. A musical piece is generally formed of one or more predefined rhythmic patterns and such patterns are composed of rhythmic cells (RC), which are groups of rhythmic figures derived from n-th division of a larger rhythmic figure. At the pre-processing and encoding phase, the RCs of drums percussion lines are represented in duration-weighted notation (DWN). Then, the vector of DWM is encoded to be free of the dimensional dependence on the number of figures in the RC. After that, a network is constructed from the encoded DWM using the method proposed in this paper. We find that the rhythmic patterns of the musical work are related to the formation of communities in the network. In this work, two community detection algorithms are used: Louvain algorithm for the disjoint community detection and Bayesian Nonnegative Matrix Factorization algorithm (BNMF) for detecting overlapping communities. Moreover, a new measure for quantifying the relevance of communities to differentiate types of rhythmic patterns is introduced. The proposed technique has been applied to automatic extraction of drums rhythmic pattern of the song "Drive my car" by The Beatles. Experimental results show good performance of the proposed method.