Neural-Base Music Generation for Intelligence Duplication
This addresses the challenge of intelligent duplication for music composition, enabling the generation of new music based on learned expertise, though it appears incremental in applying existing deep learning to a specific domain.
The paper tackles the problem of inventing new information by learning an individual's creative reasoning, specifically using a deep learning system to capture Beethoven's composition ability in a hash-based knowledge base, resulting in a novel music generation method.
There are two aspects of machine learning and artificial intelligence: (1) interpreting information, and (2) inventing new useful information. Much advance has been made for (1) with a focus on pattern recognition techniques (e.g., interpreting visual data). This paper focuses on (2) with intelligent duplication (ID) for invention. We explore the possibility of learning a specific individual's creative reasoning in order to leverage the learned expertise and talent to invent new information. More specifically, we employ a deep learning system to learn from the great composer Beethoven and capture his composition ability in a hash-based knowledge base. This new form of knowledge base provides a reasoning facility to drive the music composition through a novel music generation method.