musical knowledge extraction

The rhythmic background of a musical piece is usually composed of featured elements that define the musical genre. For each song, such elements form rhythmic patterns, the most repetitive ones defines the rhythm of a specific musical genre and the less frequent ones correspond to fortuitous patterns that comply a transition, diversification, introduction or conclusion function. In this paper, we introduce a network-based method for automatic extraction of rhythmic pattern of a single song. We also propose a method for rhythmic summarization of a set of songs from the same genre, artist, and combinations of genres and artists. The method can be used to extract any type of rhythm pattern, both monophonic and polyphonic, represented by symbolic data. A musical piece is generally formed by one or more predefined rhythmic patterns and such patterns are composed of rhythmic cells (RCs), which are groups of rhythmic figures derived from nth division of a larger rhythmic figure. At the pre-processing and encoding phases of the proposed method, the RCs of drums percussion lines are represented in duration-weighted notation (DWN). Then, the vector of DWN 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 DWN 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 constructed music network. In order to confirm such a finding, three network community detection algorithms are applied: Modularity Optimization algorithm and Louvain algorithm for the disjoint community detection, and Bayesian Non-negative Matrix Factorization algorithm for detecting overlapped communities. Furthermore, 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 rhythmic pattern and rhythmic summarization of the songs of The Beatles, Bob Marley, and other artists, respectively. The results show that the method of extraction and summarization has good performance.