Workshop Statement

Description: With the current explosion and quick expansion of music in digital formats, and the computational power of modern systems, the research on machine learning and music has gained increasing popularity. As complexity of the problems investigated by researchers on machine learning and music increases, there is a need to develop new algorithms and methods to solve these problems. Machine learning has proved to provide efficient solutions to many music-related problems both of academic and commercial interest. An example of the later is the application of machine learning techniques to the key challenge in the area of automatic generation of music material. The workshop provides a forum for theoretical discussions on machine learning for music generation, and also encourages performances of generated creative outputs as part of workshop participation. Duration: Half Day sites.google.com/site/musicmachinelearning15

• Rafael Ramirez, Universitat Pompeu Fabra, Barcelona, ES. Machine Learning, Data Mining, Data Science, Computer Science, Predictive Analytics.
• Darrell Conklin, Universidad del País Vasco, ES
• Jose Manuel Iñesta, Alicante University, ES