GUITAR SOLOS AS NETWORKS Stefano Ferretti Department of Computer Science and Engineering, University of Bologna Mura A. Zamboni 7, I-40127 Bologna, Italy
[email protected] ABSTRACT cerns music information retrieval, techniques worthy of men- tion are acoustic-based similarity measures [8], compression- This paper presents an approach to model melodies (and mu- based methods for the classification of pieces of music [9], sic pieces in general) as networks. Notes of a melody can be statistical analyses and artificial neural networks [10]. Finally, seen as nodes of a network that are connected whenever these in [11] artificial intelligence is employed to capture statisti- are played in sequence. This creates a directed graph. By cal proportions of music attributes, such as pitch, duration, using complex network theory, it is possible to extract some melodic and harmonic intervals, etc. main metrics, typical of networks, that characterize the piece. Using this framework, we provide an analysis on a set of gui- Studies on music can be based on symbolic data (music tar solos performed by main musicians. The results of this scores) or on audio recordings. Symbolic music data eases study indicate that this model can have an impact on mul- the analysis in several music application domains. For exam- timedia applications such as music classification, identifica- ple, finding the notes of a melody in an audio file can be a tion, and automatic music generation. difficult task, while with symbolic music, notes are the start- ing point for the analysis. Thus, in general traditional mu- Index Terms— Media Analysis, Musical Scores, Com- sicological concepts such as melodic and harmonic structure plex Networks are easier to investigate in the symbolic domain, and usually more successful [12].