An evaluation of musical pattern discovery algorithms using a visualisation application

Musical pattern discovery refers to the automated discovery of important repeated patterns, such as melodies and themes, from music data. Several algorithms have been developed to solve this problem, but evaluating the algorithms has been difficult without proper visualisations of the output of the algorithms. To address this issue a web application named Mupadie was built. Mupadie accepts MIDI music files as input and visualises the outputs of musical pattern discovery algorithms, with implementations of SIATEC and TTWIA built in the application. Other algorithms can be visualised if the algorithm output is uploaded to Mupadie as a JSON file that follows a specified data structure. Using Mupadie, an evaluation of SIATEC and TTWIA was conducted. Mupadie was found to be a useful tool in the qualitative evaluation of these musical pattern discovery algorithms; it helped reveal systematically recurring issues with the discovered patterns, some previously known and some previously undocumented. The findings were then used to suggest improvements to the algorithms