Abstract-Generating music with lon g-term structure is one of the main challenges in tbe field of automatie composition. This article describes MorpheuS, a music generation system. MorpheuS uses state·of-the-art pattern detection techniques to find repeated patterns in a lemplate piece. These patterns are then used to constrain the generation process for a new polyphonie composition. Tbc music generation process is guided by an efficient optimization algorithm, variable neighborhood search, wh ich uses a mathematical model of tonal tension to derive its objective function . The ability to generate music according to a ten ion profile could be userul in agame or film music context. Pieces generated by MorpheuS have been performed in live concerts. AULOmated compo. ition has made great strides in the past few decades since the advent of computers. One of the bigge t remaining challenge in the field of automatic mu ic composition is lhe crafting of long term lructure. Most computer-generated music sounds good in the short term, but lacks recurring and developing theme , and long-term form. In [1]. the authors developed an efficient variable neighborhood search (VNS) metaheuristic lhat generates music with a given semiotic structure (e.g. ABABBC). This research showed that considering mus ic generation in an optimization context offers a viable way of con training, and thu ensuring, structure. This paper expands the earlier work to polyphonie music, adding to tbe system a pattern detection algorithm and a computational model for musical tension. In the resulting ystem named MorpheuS, the pattern delection algori th m [2] finds reoccurring note patterns in a template piece. These patterns are then used to constrain structure in a newly generated piece. An early prototype by Screene and Wiggins [3] attempt to combine detected patterns with generation by Markov models. Because of the recursive nature of the pattern computation, their approach requires non-standard hierarchieal models. Further research is needed on how to construct these models and best generated from them. Our research avoids thi. problem by using an optimization approach that constrains the patterns. MorpheuS generates music according to a tension profile lhal can be computed from the lemplate piece or specified by a user. Tension shapes our experience of music listening; it is especially relevant in the context of film or game music. Figure 1 displays The next two sections give an overview of the tension model and pattern detection module. Section IV defines music generation formally as an optimizalion problem , and lhen de cribes the optimizalion algorilhm. The final ections di cuss the computationaJ and musical results. The experience of tension is an intrinsic part of music listening. The authors have developed a mathematical model for quantifying tonal tension [4] based on the spiral array a three-dimensional model of tonality [5]. To calculate tonal tension , a piece is first segmented into equal-Iength segments. The notes from each segment are mapped to a 'c\oud" of points in the spiral array. The model inc\udes three measures of tension: c\oud diameter which captures the dispersion (dis onance) of lhe nOles in tonal space; c\oud momenrum, which quantifies the degree of tonal change between c\ouds ; and tensi le strain. which captw·es the tonal distance from the c\oud center the global key.
A constrained variable neighborhood search to generate polyphonic music obeying a tension profile and the repetition structure from a template piece was proposed in REF .
MorpheuS: automatic music generation with recurrent pattern constraints and tension profiles
2016 IEEE Region 10 Conference (TENCON)
2016 IEEE Region 10 Conference (TENCON)
Computer Science