An Intelligent Interface for Drum Pattern Variation and Comparative Evaluation of Algorithms

Drum tracks of electronic dance music pieces are a central and style-defining element. Yet, creating them can be a cumbersome task, mostly due to lack of appropriate tools and input devices. In this work we use a UI prototype that aims at supporting musicians to compose the rhythmic patterns for drum tracks, to compare different algorithms for drum pattern variation. Starting with a basic pattern (seed pattern), which is provided by the user, a list of variations with varying degrees of similarity to the seed pattern is generated. The variations are created using one of the three algorithms compared: (i) a similarity-based lookup method using a rhythm pattern database, (ii) a generative approach based on a stochastic neural network, and (iii) a genetic algorithm using similarity measures as a target function. The interface visualizes the patterns and provides an intuitive way to browse through them. User test sessions with experts in electronic music production were conducted to evaluate aspects of the prototype and algorithms. Additionally a web-based survey was performed to assess perceptual properties of the variations in comparison to baseline patterns created by a human expert. The web survey shows that the algorithms produce musical and interesting variations and that the different algorithms have their strengths in different areas. These findings are further supported by the results of the expert interviews.